On this page you can find the publication list of the Machine Learning and Perception Lab.
2018
Matteo Chini; Niki Martinel; Matteo Dunnhofer; Carlo Ceschia; Christian Micheloni
Unsupervised Smoke Detection in Normally Smoking Environments Proceedings Article
In: International Conference on Distributed Smart Cameras, pp. 1–6, ACM Press, New York, New York, USA, 2018, ISBN: 9781450365116.
@inproceedings{Chini2018,
title = {Unsupervised Smoke Detection in Normally Smoking Environments},
author = {Matteo Chini and Niki Martinel and Matteo Dunnhofer and Carlo Ceschia and Christian Micheloni},
url = {http://dl.acm.org/citation.cfm?doid=3243394.3243699},
doi = {10.1145/3243394.3243699},
isbn = {9781450365116},
year = {2018},
date = {2018-01-01},
booktitle = {International Conference on Distributed Smart Cameras},
pages = {1--6},
publisher = {ACM Press},
address = {New York, New York, USA},
abstract = {The problem of smoke detection through visual analytics is an open challenging problem. The existing literature has addressed the problem by mainly working on the best feature representation and by exploiting supervised solutions which consider the prob- lem of smoke detection as a binary classification one. Differently from such works, we consider the possibility that in some contexts sensing smokes is a common situation, but want to detect when there are significative fluctuations within this normal situation. In light of such a consideration, we propose an unsupervised solu- tion that leverages on the concept of anomaly detection. Different visual representations have been used together with a temporal smoothing function reduce the effects of noisy measurement. Such temporally smoothed representations are then exploited to learn a robust ânormalityâ model by means of a One-Class Support Vector Machine. A real prototype has been developed and exploited to collect a new dataset which has been considered to evaluate the proposed solution.},
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The problem of smoke detection through visual analytics is an open challenging problem. The existing literature has addressed the problem by mainly working on the best feature representation and by exploiting supervised solutions which consider the prob- lem of smoke detection as a binary classification one. Differently from such works, we consider the possibility that in some contexts sensing smokes is a common situation, but want to detect when there are significative fluctuations within this normal situation. In light of such a consideration, we propose an unsupervised solu- tion that leverages on the concept of anomaly detection. Different visual representations have been used together with a temporal smoothing function reduce the effects of noisy measurement. Such temporally smoothed representations are then exploited to learn a robust ânormalityâ model by means of a One-Class Support Vector Machine. A real prototype has been developed and exploited to collect a new dataset which has been considered to evaluate the proposed solution.
Danilo Avola; Luigi Cinque; Gian Luca Foresti; Niki Martinel; Daniele Pannone; Claudio Piciarelli
A UAV Video Dataset for Mosaicking and Change Detection From Low-Altitude Flights Journal Article
In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–11, 2018, ISSN: 2168-2216.
@article{Avola2018a,
title = {A UAV Video Dataset for Mosaicking and Change Detection From Low-Altitude Flights},
author = {Danilo Avola and Luigi Cinque and Gian Luca Foresti and Niki Martinel and Daniele Pannone and Claudio Piciarelli},
url = {http://ieeexplore.ieee.org/document/8303666/},
doi = {10.1109/TSMC.2018.2804766},
issn = {2168-2216},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
pages = {1--11},
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Niki Martinel; Gian Luca Foresti; Christian Micheloni
Wide-Slice Residual Networks for Food Recognition Proceedings Article
In: The IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
@inproceedings{Martinel2016e,
title = {Wide-Slice Residual Networks for Food Recognition},
author = {Niki Martinel and Gian Luca Foresti and Christian Micheloni},
url = {http://arxiv.org/abs/1612.06543},
year = {2018},
date = {2018-01-01},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
abstract = {Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition algorithms. Recent works in the field are focusing either on hand-crafted representations or on learning these by exploiting deep neural networks. Despite the success of such a last family of works, these generally exploit off-the shelf deep architectures to classify food dishes. Thus, the architectures are not cast to the specific problem. We believe that better results can be obtained if the deep architecture is defined with respect to an analysis of the food composition. Following such an intuition, this work introduces a new deep scheme that is designed to handle the food structure. Specifically, inspired by the recent success of residual deep network, we exploit such a learning scheme and introduce a slice convolution block to capture the vertical food layers. Outputs of the deep residual blocks are combined with the sliced convolution to produce the classification score for specific food categories. To evaluate our proposed architecture we have conducted experimental results on three benchmark datasets. Results demonstrate that our solution shows better performance with respect to existing approaches (e.g., a top-1 accuracy of 90.27% on the Food-101 challenging dataset).},
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Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition algorithms. Recent works in the field are focusing either on hand-crafted representations or on learning these by exploiting deep neural networks. Despite the success of such a last family of works, these generally exploit off-the shelf deep architectures to classify food dishes. Thus, the architectures are not cast to the specific problem. We believe that better results can be obtained if the deep architecture is defined with respect to an analysis of the food composition. Following such an intuition, this work introduces a new deep scheme that is designed to handle the food structure. Specifically, inspired by the recent success of residual deep network, we exploit such a learning scheme and introduce a slice convolution block to capture the vertical food layers. Outputs of the deep residual blocks are combined with the sliced convolution to produce the classification score for specific food categories. To evaluate our proposed architecture we have conducted experimental results on three benchmark datasets. Results demonstrate that our solution shows better performance with respect to existing approaches (e.g., a top-1 accuracy of 90.27% on the Food-101 challenging dataset).
Niki Martinel
Accelerated low-rank sparse metric learning for person re-identification Journal Article
In: Pattern Recognition Letters, vol. 112, pp. 234–240, 2018, ISSN: 01678655.
@article{Martinel2018a,
title = {Accelerated low-rank sparse metric learning for person re-identification},
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url = {https://doi.org/10.1016/j.patrec.2018.07.033},
doi = {10.1016/j.patrec.2018.07.033},
issn = {01678655},
year = {2018},
date = {2018-01-01},
journal = {Pattern Recognition Letters},
volume = {112},
pages = {234--240},
publisher = {Elsevier B.V.},
abstract = {Person re-identification is an open and challenging problem in computer vision. A surge of effort has been spent design the best feature representation, and to learn either the transformation of such features across cameras or an optimal matching metric. Metric learning solutions which are currently in vogue in the field generally require a dimensionality reduction pre-processing stage to handle the high-dimensionality of the adopted feature representation. Such an approach is suboptimal and a better solution can be achieved by combining such a step in the metric learning process. Towards this objective, a low-rank matrix which projects the high-dimensional vectors to a low-dimensional manifold with a discriminative Euclidean distance is introduced. The goal is achieved with a stochastic accelerated proximal gradient method. Experiments on two public benchmark datasets show that better performances than state-of-the-art methods are achieved.},
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Person re-identification is an open and challenging problem in computer vision. A surge of effort has been spent design the best feature representation, and to learn either the transformation of such features across cameras or an optimal matching metric. Metric learning solutions which are currently in vogue in the field generally require a dimensionality reduction pre-processing stage to handle the high-dimensionality of the adopted feature representation. Such an approach is suboptimal and a better solution can be achieved by combining such a step in the metric learning process. Towards this objective, a low-rank matrix which projects the high-dimensional vectors to a low-dimensional manifold with a discriminative Euclidean distance is introduced. The goal is achieved with a stochastic accelerated proximal gradient method. Experiments on two public benchmark datasets show that better performances than state-of-the-art methods are achieved.
Niki Martinel; Gian Luca Foresti; Christian Micheloni
Unsupervised hashing with neural trees for image retrieval and person re-identification Conference
2018, (Cited by: 1; All Open Access, Green Open Access).
@conference{Martinel2018c,
title = {Unsupervised hashing with neural trees for image retrieval and person re-identification},
author = {Niki Martinel and Gian Luca Foresti and Christian Micheloni},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056626578&doi=10.1145%2f3243394.3243687&partnerID=40&md5=c92d1adb95b41eba52c06e72a81b9644},
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year = {2018},
date = {2018-01-01},
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Niki Martinel; Gian Luca Foresti; Christian Micheloni
Wide-slice residual networks for food recognition Conference
vol. 2018-January, 2018, (Cited by: 167; All Open Access, Green Open Access).
@conference{Martinel2018567,
title = {Wide-slice residual networks for food recognition},
author = {Niki Martinel and Gian Luca Foresti and Christian Micheloni},
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doi = {10.1109/WACV.2018.00068},
year = {2018},
date = {2018-01-01},
journal = {Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018},
volume = {2018-January},
pages = {567 – 576},
note = {Cited by: 167; All Open Access, Green Open Access},
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2017
Niki Martinel; Gian Luca Foresti; Christian Micheloni
Person Reidentification in a Distributed Camera Network Framework Journal Article
In: IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3530–3541, 2017, ISSN: 2168-2267.
@article{Martinel2016a,
title = {Person Reidentification in a Distributed Camera Network Framework},
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doi = {10.1109/TCYB.2016.2568264},
issn = {2168-2267},
year = {2017},
date = {2017-11-01},
journal = {IEEE Transactions on Cybernetics},
volume = {47},
number = {11},
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Jorge Garcia; Niki Martinel; Alfredo Gardel; Ignacio Bravo; Gian Luca Foresti; Christian Micheloni
Discriminant Context Information Analysis for Post-Ranking Person Re-Identification Journal Article
In: IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1650–1665, 2017, ISSN: 1057-7149.
@article{Garcia2017,
title = {Discriminant Context Information Analysis for Post-Ranking Person Re-Identification},
author = {Jorge Garcia and Niki Martinel and Alfredo Gardel and Ignacio Bravo and Gian Luca Foresti and Christian Micheloni},
url = {http://ieeexplore.ieee.org/document/7815412/},
doi = {10.1109/TIP.2017.2652725},
issn = {1057-7149},
year = {2017},
date = {2017-04-01},
journal = {IEEE Transactions on Image Processing},
volume = {26},
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Niki Martinel; Gian Luca Foresti; Matteo Dunnhofer; Christian Micheloni
Person re-identification via unsupervised transfer of learned visual representations Conference
vol. Part F132201, 2017, (Cited by: 10).
@conference{Martinel2017151,
title = {Person re-identification via unsupervised transfer of learned visual representations},
author = {Niki Martinel and Gian Luca Foresti and Matteo Dunnhofer and Christian Micheloni},
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Zahid Akhtar; Christian Micheloni; Gian Luca Foresti
Biometric antispoofing on mobile devices Book
2017, (Cited by: 0).
@book{Akhtar2017375,
title = {Biometric antispoofing on mobile devices},
author = {Zahid Akhtar and Christian Micheloni and Gian Luca Foresti},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115726066&doi=10.1049%2fPBSE003E_ch15&partnerID=40&md5=2faad144dbcf8f5ea81cc7392685703c},
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year = {2017},
date = {2017-01-01},
journal = {Mobile Biometrics},
pages = {375 – 406},
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Niki Martinel; Claudio Piciarelli; Christian Micheloni
An Ensemble Feature Method for Food Classification Journal Article
In: Machine Graphics and Vision, vol. 26, no. 1, pp. 13––39, 2017.
@article{Martinel2017,
title = {An Ensemble Feature Method for Food Classification},
author = {Niki Martinel and Claudio Piciarelli and Christian Micheloni},
url = {http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-9bc56afd-1d03-420b-a0bb-492cfd82ab5c},
year = {2017},
date = {2017-01-01},
journal = {Machine Graphics and Vision},
volume = {26},
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Niki Martinel; Matteo Dunnhofer; Gian Luca Foresti; Christian Micheloni
Person Re-Identification via Unsupervised Transfer of Learned Visual Representations Proceedings Article
In: International Conference on Distributed Smart Cameras, pp. 1–6, Stanford, CA, USA, 2017, ISBN: 9781450354875.
@inproceedings{Martinel2017b,
title = {Person Re-Identification via Unsupervised Transfer of Learned Visual Representations},
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Matteo Chini; Niki Martinel; Stefano Spagnul; Christian Micheloni
Temporally Smoothed Anomaly Detection in Continuous Fluids Proceedings Article
In: International Conference on Distributed Smart Cameras, pp. 7–12, Stanford, CA, USA, 2017.
@inproceedings{Chini2017,
title = {Temporally Smoothed Anomaly Detection in Continuous Fluids},
author = {Matteo Chini and Niki Martinel and Stefano Spagnul and Christian Micheloni},
url = {https://dl.acm.org/doi/10.1145/3131885.3131924},
year = {2017},
date = {2017-01-01},
booktitle = {International Conference on Distributed Smart Cameras},
pages = {7--12},
address = {Stanford, CA, USA},
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Giuseppe Lisanti; Niki Martinel; Alberto Del Bimbo; Gian Luca Foresti
Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding Proceedings Article
In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2449–2458, Venice, Italy, 2017.
@inproceedings{Lisanti2017,
title = {Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding},
author = {Giuseppe Lisanti and Niki Martinel and Alberto {Del Bimbo} and Gian Luca Foresti},
url = {http://openaccess.thecvf.com/content_iccv_2017/html/Lisanti_Group_Re-Identification_via_ICCV_2017_paper.html},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
pages = {2449--2458},
address = {Venice, Italy},
abstract = {Person re-identification is best known as the problem of associating a single person that is observed from one or more disjoint cameras. The existing literature has mainly addressed such an issue, neglecting the fact that people usually move in groups, like in crowded scenarios. We be- lieve that the additional information carried by neighboring individuals provides a relevant visual context that can be exploited to obtain a more robust match of single persons within the group. Despite this, re-identifying groups of peo- ple compound the common single person re-identification problems by introducing changes in the relative position of persons within the group and severe self-occlusions. In this paper, we propose a solution for group re-identification that grounds on transferring knowledge from single person re- identification to group re-identification by exploiting sparse dictionary learning. First, a dictionary of sparse atoms is learned using patches extracted from single person im- ages. Then, the learned dictionary is exploited to obtain a sparsity-driven residual group representation, which is fi- nally matched to perform the re-identification. Extensive experiments on the i-LIDS groups and two newly collected datasets show that the proposed solution outperforms state- of-the-art approaches.},
keywords = {},
pubstate = {published},
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Person re-identification is best known as the problem of associating a single person that is observed from one or more disjoint cameras. The existing literature has mainly addressed such an issue, neglecting the fact that people usually move in groups, like in crowded scenarios. We be- lieve that the additional information carried by neighboring individuals provides a relevant visual context that can be exploited to obtain a more robust match of single persons within the group. Despite this, re-identifying groups of peo- ple compound the common single person re-identification problems by introducing changes in the relative position of persons within the group and severe self-occlusions. In this paper, we propose a solution for group re-identification that grounds on transferring knowledge from single person re- identification to group re-identification by exploiting sparse dictionary learning. First, a dictionary of sparse atoms is learned using patches extracted from single person im- ages. Then, the learned dictionary is exploited to obtain a sparsity-driven residual group representation, which is fi- nally matched to perform the re-identification. Extensive experiments on the i-LIDS groups and two newly collected datasets show that the proposed solution outperforms state- of-the-art approaches.
Danilo Avola; Gian Luca Foresti; Niki Martinel; Christian Micheloni; Daniele Pannone; Claudio Piciarelli
Aerial video surveillance system for small-scale UAV environment monitoring Conference
2017, (Cited by: 41).
@conference{Avola2017,
title = {Aerial video surveillance system for small-scale UAV environment monitoring},
author = {Danilo Avola and Gian Luca Foresti and Niki Martinel and Christian Micheloni and Daniele Pannone and Claudio Piciarelli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039924155&doi=10.1109%2fAVSS.2017.8078523&partnerID=40&md5=623ed7774283270210a86a63b3fd7bf9},
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Matteo Chini; Stefano Spagnul; Niki Martinel; Christian Micheloni
Temporally smoothed anomaly detection in continuous fluids Conference
vol. Part F132201, 2017, (Cited by: 0).
@conference{Chini201776,
title = {Temporally smoothed anomaly detection in continuous fluids},
author = {Matteo Chini and Stefano Spagnul and Niki Martinel and Christian Micheloni},
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Niki Martinel; Gian Luca Foresti; Christian Micheloni
Person Reidentification in a Distributed Camera Network Framework Journal Article
In: IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3530 – 3541, 2017, (Cited by: 31).
@article{Martinel20173530,
title = {Person Reidentification in a Distributed Camera Network Framework},
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Jorge Garcia; Niki Martinel; Alfredo Gardel; Ignacio Bravo; Gian Luca Foresti; Christian Micheloni
Discriminant context information analysis for post-ranking person re-identification Journal Article
In: IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1650 – 1665, 2017, (Cited by: 64).
@article{Garcia20171650,
title = {Discriminant context information analysis for post-ranking person re-identification},
author = {Jorge Garcia and Niki Martinel and Alfredo Gardel and Ignacio Bravo and Gian Luca Foresti and Christian Micheloni},
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Yakusheva Nadezda; Gian Luca Foresti; Christian Micheloni
2017, (Cited by: 6; All Open Access, Green Open Access, Hybrid Gold Open Access).
@conference{Nadezda2017352,
title = {An ADAS design based on IoT V2X communications to improve safety: Case study and iot architecture reference model},
author = {Yakusheva Nadezda and Gian Luca Foresti and Christian Micheloni},
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Niki Martinel; Claudio Piciarelli; Christian Micheloni
An ensemble feature method for food classification Journal Article
In: Machine Graphics and Vision, vol. 26, no. 1-4, pp. 13 – 39, 2017, (Cited by: 2).
@article{Martinel201713,
title = {An ensemble feature method for food classification},
author = {Niki Martinel and Claudio Piciarelli and Christian Micheloni},
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Danilo Avola; Gian Luca Foresti; Niki Martinel; Christian Micheloni; Daniele Pannone; Claudio Piciarelli
Real-time incremental and geo-referenced mosaicking by small-scale uavs Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10484 LNCS, pp. 694 – 705, 2017, (Cited by: 22).
@article{Avola2017694,
title = {Real-time incremental and geo-referenced mosaicking by small-scale uavs},
author = {Danilo Avola and Gian Luca Foresti and Niki Martinel and Christian Micheloni and Daniele Pannone and Claudio Piciarelli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032502151&doi=10.1007%2f978-3-319-68560-1_62&partnerID=40&md5=4e91900a9f0d34f95a761c201b246403},
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journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {10484 LNCS},
pages = {694 – 705},
note = {Cited by: 22},
keywords = {},
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2016
Jorge García; Niki Martinel; Alfredo Gardel; Ignacio Bravo; Gian Luca Foresti; Christian Micheloni
Modeling feature distances by orientation driven classifiers for person re-identification Journal Article
In: Journal of Visual Communication and Image Representation, vol. 38, pp. 115–129, 2016, ISSN: 10473203.
@article{Garcia2016,
title = {Modeling feature distances by orientation driven classifiers for person re-identification},
author = {Jorge García and Niki Martinel and Alfredo Gardel and Ignacio Bravo and Gian Luca Foresti and Christian Micheloni},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1047320316000353},
doi = {10.1016/j.jvcir.2016.02.009},
issn = {10473203},
year = {2016},
date = {2016-07-01},
journal = {Journal of Visual Communication and Image Representation},
volume = {38},
pages = {115--129},
abstract = {To tackle the re-identification challenges existing methods propose to directly match image features or to learn the transformation of features that undergoes between two cameras. Other methods learn optimal similarity measures. However, the performance of all these methods are strongly dependent from the person pose and orientation. We focus on this aspect and introduce three main contributions to the field: (i) to propose a method to extract multiple frames of the same person with different orientations in order to capture the complete person appearance; (ii) to learn the pairwise feature dissimilarities space (PFDS) formed by the subspaces of similar and different image pair orientations; and (iii) within each subspace, a classifier is trained to capture the multi-modal inter-camera transformation of pairwise image dissimilarities and to discriminate between positive and negative pairs. The experiments show the superior performance of the proposed approach with respect to state-of-the-art methods using two publicly available benchmark datasets.},
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To tackle the re-identification challenges existing methods propose to directly match image features or to learn the transformation of features that undergoes between two cameras. Other methods learn optimal similarity measures. However, the performance of all these methods are strongly dependent from the person pose and orientation. We focus on this aspect and introduce three main contributions to the field: (i) to propose a method to extract multiple frames of the same person with different orientations in order to capture the complete person appearance; (ii) to learn the pairwise feature dissimilarities space (PFDS) formed by the subspaces of similar and different image pair orientations; and (iii) within each subspace, a classifier is trained to capture the multi-modal inter-camera transformation of pairwise image dissimilarities and to discriminate between positive and negative pairs. The experiments show the superior performance of the proposed approach with respect to state-of-the-art methods using two publicly available benchmark datasets.
Niki Martinel; Claudio Piciarelli; Christian Micheloni
A supervised extreme learning committee for food recognition Journal Article
In: Computer Vision and Image Understanding, vol. 148, pp. 67 – 86, 2016, (Cited by: 48; All Open Access, Green Open Access).
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Niki Martinel; Claudio Piciarelli; Christian Micheloni
A supervised extreme learning committee for food recognition Journal Article
In: Computer Vision and Image Understanding, vol. 148, pp. 67–86, 2016, ISSN: 1090235X.
@article{Martinel2016d,
title = {A supervised extreme learning committee for food recognition},
author = {Niki Martinel and Claudio Piciarelli and Christian Micheloni},
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abstract = {Food recognition is an emerging topic in computer vision. The problem is being addressed especially in health-oriented systems where it is used as a support for food diary applications. The goal is to improve current food diaries, where the users have to manually insert their daily food intake, with an automatic recognition of the food type, quantity and consequent calories intake estimation. In addition to the classical recognition challenges, the food recognition problem is characterized by the absence of a rigid structure of the food and by large intra-class variations. To tackle such challenges, a food recognition system based on a committee classification is proposed. The aim is to provide a system capable of automatically choosing the optimal features for food recognition out of the existing plethora of available ones (e.g., color, texture, etc.). Following this idea, each committee member, i.e., an Extreme Learning Machine, is trained to specialize on a single feature type. Then, a Structural Support Vector Machine is exploited to produce the final ranking of possible matches by filtering out the irrelevant features and thus merging only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on four publicly available benchmark datasets.},
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Food recognition is an emerging topic in computer vision. The problem is being addressed especially in health-oriented systems where it is used as a support for food diary applications. The goal is to improve current food diaries, where the users have to manually insert their daily food intake, with an automatic recognition of the food type, quantity and consequent calories intake estimation. In addition to the classical recognition challenges, the food recognition problem is characterized by the absence of a rigid structure of the food and by large intra-class variations. To tackle such challenges, a food recognition system based on a committee classification is proposed. The aim is to provide a system capable of automatically choosing the optimal features for food recognition out of the existing plethora of available ones (e.g., color, texture, etc.). Following this idea, each committee member, i.e., an Extreme Learning Machine, is trained to specialize on a single feature type. Then, a Structural Support Vector Machine is exploited to produce the final ranking of possible matches by filtering out the irrelevant features and thus merging only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on four publicly available benchmark datasets.
Niki Martinel; Claudio Piciarelli; Gian Luca Foresti; Christian Micheloni
Mobile Food Recognition with an Extreme Deep Tree Proceedings Article
In: International Conference on Distributed Smart Cameras, pp. 56––61, Paris, France, 2016, ISBN: 9781450347860.
@inproceedings{Martinel2016c,
title = {Mobile Food Recognition with an Extreme Deep Tree},
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abstract = {Food recognition is an emerging topic in the eld of computer vision. The recent interest of the research community in this area is justi ed by the rise in popularity of food diary applications, where the users take note of their food intake for self-monitoring or to provide useful statistics to dietitians. However, manually annotating food intake can be a tedious task, thus explaining the need of a system that automatically recognizes food, and possibly its amount, from pictures acquired by mobile devices. In this work we propose an approach to food recognition which combines the strengths of di erent state-of-the-art classi ers, namely Convolutional Neural Networks, Extreme Learning Machines and Neural Trees. We show that the proposed architecture can achieve good results even with low computational power, as in the case of mobile devices.},
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Food recognition is an emerging topic in the eld of computer vision. The recent interest of the research community in this area is justi ed by the rise in popularity of food diary applications, where the users take note of their food intake for self-monitoring or to provide useful statistics to dietitians. However, manually annotating food intake can be a tedious task, thus explaining the need of a system that automatically recognizes food, and possibly its amount, from pictures acquired by mobile devices. In this work we propose an approach to food recognition which combines the strengths of di erent state-of-the-art classi ers, namely Convolutional Neural Networks, Extreme Learning Machines and Neural Trees. We show that the proposed architecture can achieve good results even with low computational power, as in the case of mobile devices.
Niki Martinel; Gian Luca Foresti; Christian Micheloni
Distributed and Unsupervised Cost-Driven Person Re-identification Proceedings Article
In: International Conference on Pattern Recognition (ICPR), pp. 1225–1230, Cancun, Mexico, 2016.
@inproceedings{Martinel2016b,
title = {Distributed and Unsupervised Cost-Driven Person Re-identification},
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abstract = {The problem of re-identify persons across single disjoint camera-pairs has received great attention from the community. Despite this, when the re-identification process has to be carried out on a large camera network a different approach has to be considered. In particular, existing approaches have neglected the importance of the network topology (i.e., the structure of the monitored environment) in such a process. To try filling such a gap, we propose a Distributed and Unsupervised Cost-Driven Person Re-Identification framework (DUPRe) which introduces the following contributions: (i) a camera matching cost to measure the re-identification performance between nodes of the network; (ii) a derivation of the distance vector algorithm which allows to learn the network topology hence to prioritize and limit the cameras inquired for the re-identification. Results on two benchmark datasets show that our solution brings to significant network-wise re-identification improvements.},
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The problem of re-identify persons across single disjoint camera-pairs has received great attention from the community. Despite this, when the re-identification process has to be carried out on a large camera network a different approach has to be considered. In particular, existing approaches have neglected the importance of the network topology (i.e., the structure of the monitored environment) in such a process. To try filling such a gap, we propose a Distributed and Unsupervised Cost-Driven Person Re-Identification framework (DUPRe) which introduces the following contributions: (i) a camera matching cost to measure the re-identification performance between nodes of the network; (ii) a derivation of the distance vector algorithm which allows to learn the network topology hence to prioritize and limit the cameras inquired for the re-identification. Results on two benchmark datasets show that our solution brings to significant network-wise re-identification improvements.
Niki Martinel; Abir Das; Christian Micheloni; Amit K Roy-Chowdhury
Temporal Model Adaptation for Person Re-Identification Proceedings Article
In: European Conference on Computer Vision (ECCV), pp. 858–877, Springer, Amsterdam, The Netherlands, 2016.
@inproceedings{Martinel2016f,
title = {Temporal Model Adaptation for Person Re-Identification},
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abstract = {Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%.},
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Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%.
Niki Martinel; Christian Micheloni; Gian Luca Foresti
A Pool of Multiple Person Re-Identification Experts Journal Article
In: Pattern Recognition Letters, vol. 71, pp. 23–30, 2016, ISSN: 01678655.
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Niki Martinel; Claudio Piciarelli; Christian Micheloni; Gian Luca Foresti
A Structured Committee for Food Recognition Conference
vol. 2015-February, 2016, (Cited by: 15; All Open Access, Green Open Access).
@conference{Martinel2016484,
title = {A Structured Committee for Food Recognition},
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Jorge García; Niki Martinel; Alfredo Gardel; Ignacio Bravo; Gian Luca Foresti; Christian Micheloni
Modeling feature distances by orientation driven classifiers for person re-identification Journal Article
In: Journal of Visual Communication and Image Representation, vol. 38, pp. 115 – 129, 2016, (Cited by: 28; All Open Access, Green Open Access).
@article{García2016115,
title = {Modeling feature distances by orientation driven classifiers for person re-identification},
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Jorge Fernández-Berni; François Berry; Christian Micheloni
Special Section Guest Editorial:Advances on Distributed Smart Cameras Journal Article
In: Journal of Electronic Imaging, vol. 25, no. 4, 2016, (Cited by: 0; All Open Access, Bronze Open Access).
@article{Fernández-Berni2016,
title = {Special Section Guest Editorial:Advances on Distributed Smart Cameras},
author = {Jorge Fernández-Berni and François Berry and Christian Micheloni},
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Niki Martinel; Claudio Piciarelli; Gian Luca Foresti; Christian Micheloni
Mobile food recognition with an extreme deep tree Conference
vol. 12-15-September-2016, 2016, (Cited by: 11).
@conference{Martinel201656,
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Zahid Akhtar; Christian Micheloni; Gian Luca Foresti
Mobile ocular biometrics in visible spectrum using local image descriptors: A preliminary study Conference
vol. 2016-August, 2016, (Cited by: 5).
@conference{Akhtar2016340,
title = {Mobile ocular biometrics in visible spectrum using local image descriptors: A preliminary study},
author = {Zahid Akhtar and Christian Micheloni and Gian Luca Foresti},
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Niki Martinel; Christian Micheloni; Gian Luca Foresti
A pool of multiple person re-identification experts Journal Article
In: Pattern Recognition Letters, vol. 71, pp. 23 – 30, 2016, (Cited by: 20).
@article{Martinel201623,
title = {A pool of multiple person re-identification experts},
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Niki Martinel; Abir Das; Christian Micheloni; Amit K. Roy-Chowdhury
Temporal model adaptation for person re-identification Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9908 LNCS, pp. 88 – 104, 2016, (Cited by: 11).
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title = {Temporal model adaptation for person re-identification},
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Niki Martinel; Gian Luca Foresti; Christian Micheloni
Distributed and Unsupervised Cost-Driven Person Re-Identification Conference
vol. 0, 2016, (Cited by: 4).
@conference{Martinel20161225,
title = {Distributed and Unsupervised Cost-Driven Person Re-Identification},
author = {Niki Martinel and Gian Luca Foresti and Christian Micheloni},
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2015
Niki Martinel; Christian Micheloni; Gian Luca Foresti
Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning Journal Article
In: IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5645–5658, 2015, ISSN: 1057-7149.
@article{Martinel2015cb,
title = {Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning},
author = {Niki Martinel and Christian Micheloni and Gian Luca Foresti},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7289409},
doi = {10.1109/TIP.2015.2487048},
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year = {2015},
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journal = {IEEE Transactions on Image Processing},
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abstract = {Person re-identification in a non-overlapping multi-camera scenario is an open and interesting challenge.While the task can hardly be completed by machines, we, as humans, are inherently able to sample those relevant persons' details that allow us to correctly solve the problem in a fraction of a second. Thus, knowing where a human might fixate to recognize a person is of paramount interest for re-identification. Inspired by the human gazing capabilities, we want to identify the salient regions of a person appearance to tackle the problem. Toward this objective, we introduce the following main contributions. A kernelized graph-based approach is used to detect the salient regions of a person appearance, later used as a weighting tool in the feature extraction process. The proposed person representation combines visual features either considering or not the saliency. These are then exploited in a pairwise-based multiple metric learning framework. Finally, the non-Euclidean metrics that have been separately learned for each feature are fused to re-identify a person. The proposed kernelized saliency- based person re-identification through multiple metric learning has been evaluated on four publicly available benchmark data sets to show its superior performance over the state-of-the-art approaches (e.g., it achieves a rank 1 correct recognition rate of 42.41% on the VIPeR dataset).},
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Person re-identification in a non-overlapping multi-camera scenario is an open and interesting challenge.While the task can hardly be completed by machines, we, as humans, are inherently able to sample those relevant persons' details that allow us to correctly solve the problem in a fraction of a second. Thus, knowing where a human might fixate to recognize a person is of paramount interest for re-identification. Inspired by the human gazing capabilities, we want to identify the salient regions of a person appearance to tackle the problem. Toward this objective, we introduce the following main contributions. A kernelized graph-based approach is used to detect the salient regions of a person appearance, later used as a weighting tool in the feature extraction process. The proposed person representation combines visual features either considering or not the saliency. These are then exploited in a pairwise-based multiple metric learning framework. Finally, the non-Euclidean metrics that have been separately learned for each feature are fused to re-identify a person. The proposed kernelized saliency- based person re-identification through multiple metric learning has been evaluated on four publicly available benchmark data sets to show its superior performance over the state-of-the-art approaches (e.g., it achieves a rank 1 correct recognition rate of 42.41% on the VIPeR dataset).
Niki Martinel; Abir Das; Christian Micheloni; Amit K Roy-Chowdhury
Re-Identification in the Function Space of Feature Warps Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 37, no. 8, pp. 1656–1669, 2015, ISSN: 0162-8828.
@article{Martinel2015a,
title = {Re-Identification in the Function Space of Feature Warps},
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Niki Martinel; Christian Micheloni; Gian Luca Foresti
The Evolution of Neural Learning Systems: A Novel Architecture Combining the Strengths of NTs, CNNs, and ELMs Journal Article
In: IEEE Systems, Man, and Cybernetics Magazine, vol. 1, no. 3, pp. 17–26, 2015, ISSN: 2333-942X.
@article{Martinel2015b,
title = {The Evolution of Neural Learning Systems: A Novel Architecture Combining the Strengths of NTs, CNNs, and ELMs},
author = {Niki Martinel and Christian Micheloni and Gian Luca Foresti},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7426555},
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Niki Martinel; Christian Micheloni
Classification of Local Eigen-Dissimilarities for Person Re-Identification Journal Article
In: IEEE Signal Processing Letters, vol. 22, no. 4, pp. 455–459, 2015, ISSN: 1070-9908.
@article{Martinel2014,
title = {Classification of Local Eigen-Dissimilarities for Person Re-Identification},
author = {Niki Martinel and Christian Micheloni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6919267 http://ieeexplore.ieee.org/document/6919267/},
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Niki Martinel; Danilo Avola; Claudio Piciarelli; Christian Micheloni; Marco Vernier; Luigi Cinque; Gian Luca Foresti
Selection of temporal features for event detection in smart security Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9280, pp. 609 – 619, 2015, (Cited by: 2; All Open Access, Bronze Open Access).
@article{Martinel2015609,
title = {Selection of temporal features for event detection in smart security},
author = {Niki Martinel and Danilo Avola and Claudio Piciarelli and Christian Micheloni and Marco Vernier and Luigi Cinque and Gian Luca Foresti},
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pages = {609 – 619},
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Niki Martinel; Abir Das; Christian Micheloni; Amit K. Roy-Chowdhury
Re-identification in the function space of feature warps Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 8, pp. 1656 – 1669, 2015, (Cited by: 58; All Open Access, Bronze Open Access).
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date = {2015-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {37},
number = {8},
pages = {1656 – 1669},
note = {Cited by: 58; All Open Access, Bronze Open Access},
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N Martinel; C Piciarelli; C Micheloni; G L Foresti
On filter banks of texture features for mobile food classification Proceedings Article
In: ACM International Conference Proceeding Series, 2015, ISBN: 9781450336819.
@inproceedings{Martinel2015g,
title = {On filter banks of texture features for mobile food classification},
author = {N Martinel and C Piciarelli and C Micheloni and G L Foresti},
url = {https://dl.acm.org/doi/abs/10.1145/2789116.2789132},
doi = {10.1145/2789116.2789132},
isbn = {9781450336819},
year = {2015},
date = {2015-01-01},
booktitle = {ACM International Conference Proceeding Series},
volume = {08-11-Sep-},
abstract = {textcopyright 2015 ACM. Nowadays obesity has become one of the most common diseases in many countries. To face it, obese people should constantly monitor their daily meals both for self-limitation and to provide useful statistics for their dietitians. This has led to the recent rise in popularity of food diary applications on mobile devices, where the users can manually annotate their food intake. To overcome the tediousness of such a process, several works on automatic image food recognition have been proposed, typically based on texture features extraction and classification. In this work, we analyze different texture filter banks to evaluate their performances and propose a method to automatically aggregate the best features for food classification purposes. Particular emphasis is put in the computational burden of the system to match the limited capabilities of mobile devices.},
keywords = {},
pubstate = {published},
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textcopyright 2015 ACM. Nowadays obesity has become one of the most common diseases in many countries. To face it, obese people should constantly monitor their daily meals both for self-limitation and to provide useful statistics for their dietitians. This has led to the recent rise in popularity of food diary applications on mobile devices, where the users can manually annotate their food intake. To overcome the tediousness of such a process, several works on automatic image food recognition have been proposed, typically based on texture features extraction and classification. In this work, we analyze different texture filter banks to evaluate their performances and propose a method to automatically aggregate the best features for food classification purposes. Particular emphasis is put in the computational burden of the system to match the limited capabilities of mobile devices.
J Garcia; N Martinel; C Micheloni; A Gardel
Person re-identification ranking optimisation by discriminant context information analysis Proceedings Article
In: Proceedings of the International Conference on Computer Vision (ICCV), 2015, ISSN: 15505499.
@inproceedings{Garcia2015a,
title = {Person re-identification ranking optimisation by discriminant context information analysis},
author = {J Garcia and N Martinel and C Micheloni and A Gardel},
doi = {10.1109/ICCV.2015.154},
issn = {15505499},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the International Conference on Computer Vision (ICCV)},
volume = {2015 Inter},
abstract = {textcopyright 2015 IEEE. Person re-identification is an open and challenging problem in computer vision. Existing re-identification approaches focus on optimal methods for features matching (e.g., metric learning approaches) or study the inter-camera transformations of such features. These methods hardly ever pay attention to the problem of visual ambiguities shared between the first ranks. In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis. The proposed approach refines a given initial ranking by removing the visual ambiguities common to first ranks. This is achieved by analyzing their content and context information. Extensive experiments on three publicly available benchmark datasets and different baseline methods have been conducted. Results demonstrate a remarkable improvement in the first positions of the ranking. Regardless of the selected dataset, state-of-the-art methods are strongly outperformed by our method.},
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textcopyright 2015 IEEE. Person re-identification is an open and challenging problem in computer vision. Existing re-identification approaches focus on optimal methods for features matching (e.g., metric learning approaches) or study the inter-camera transformations of such features. These methods hardly ever pay attention to the problem of visual ambiguities shared between the first ranks. In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis. The proposed approach refines a given initial ranking by removing the visual ambiguities common to first ranks. This is achieved by analyzing their content and context information. Extensive experiments on three publicly available benchmark datasets and different baseline methods have been conducted. Results demonstrate a remarkable improvement in the first positions of the ranking. Regardless of the selected dataset, state-of-the-art methods are strongly outperformed by our method.
Niki Martinel; Claudio Piciarelli; Christian Micheloni; Gian Luca Foresti
A Structured Committee for Food Recognition Proceedings Article
In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, pp. 92–100, Santiago, Chile, 2015.
@inproceedings{Martinel2015c,
title = {A Structured Committee for Food Recognition},
author = {Niki Martinel and Claudio Piciarelli and Christian Micheloni and Gian Luca Foresti},
url = {http://openaccess.thecvf.com/content_iccv_2015_workshops/w12/html/Martinel_A_Structured_Committee_ICCV_2015_paper.html},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
pages = {92--100},
address = {Santiago, Chile},
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Niki Martinel; Claudio Piciarelli; Christian Micheloni; Gian Luca Foresti
On Filter Banks of Texture Features for Mobile Food Classification Proceedings Article
In: International Conference on Distributed Smart Cameras, pp. 11–16, Seville, Spain, 2015, ISBN: 9781450336819.
@inproceedings{Martinel2015e,
title = {On Filter Banks of Texture Features for Mobile Food Classification},
author = {Niki Martinel and Claudio Piciarelli and Christian Micheloni and Gian Luca Foresti},
url = {https://dl.acm.org/doi/10.1145/2789116.2789132},
isbn = {9781450336819},
year = {2015},
date = {2015-01-01},
booktitle = {International Conference on Distributed Smart Cameras},
pages = {11--16},
address = {Seville, Spain},
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pubstate = {published},
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Zahid Akhtar; Christian Micheloni; Gian Luca Foresti
Correlation based fingerprint liveness detection Conference
2015, (Cited by: 19).
@conference{Akhtar2015305,
title = {Correlation based fingerprint liveness detection},
author = {Zahid Akhtar and Christian Micheloni and Gian Luca Foresti},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943302622&doi=10.1109%2fICB.2015.7139054&partnerID=40&md5=4dea26c76f326345c1232b08790cfc17},
doi = {10.1109/ICB.2015.7139054},
year = {2015},
date = {2015-01-01},
journal = {Proceedings of 2015 International Conference on Biometrics, ICB 2015},
pages = {305 – 310},
note = {Cited by: 19},
keywords = {},
pubstate = {published},
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Asha Rani; Gian Luca Foresti; Christian Micheloni
A neural tree for classification using convex objective function Journal Article
In: Pattern Recognition Letters, vol. 68, no. P1, pp. 41 – 47, 2015, (Cited by: 11).
@article{Rani201541,
title = {A neural tree for classification using convex objective function},
author = {Asha Rani and Gian Luca Foresti and Christian Micheloni},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941785777&doi=10.1016%2fj.patrec.2015.08.017&partnerID=40&md5=df8de606ef9a6c79d71dba1d4d89872a},
doi = {10.1016/j.patrec.2015.08.017},
year = {2015},
date = {2015-01-01},
journal = {Pattern Recognition Letters},
volume = {68},
number = {P1},
pages = {41 – 47},
note = {Cited by: 11},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Niki Martinel; Christian Micheloni; Gian Luca Foresti
Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning Journal Article
In: IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5645 – 5658, 2015, (Cited by: 97).
@article{Martinel20155645,
title = {Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning},
author = {Niki Martinel and Christian Micheloni and Gian Luca Foresti},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945274812&doi=10.1109%2fTIP.2015.2487048&partnerID=40&md5=da4eae4094c927bfd19ddb241141208a},
doi = {10.1109/TIP.2015.2487048},
year = {2015},
date = {2015-01-01},
journal = {IEEE Transactions on Image Processing},
volume = {24},
number = {12},
pages = {5645 – 5658},
note = {Cited by: 97},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Zahid Akhtar; Christian Micheloni; Gian Luca Foresti
Biometric Liveness Detection: Challenges and Research Opportunities Journal Article
In: IEEE Security and Privacy, vol. 13, no. 5, pp. 63 – 72, 2015, (Cited by: 108).
@article{Akhtar201563,
title = {Biometric Liveness Detection: Challenges and Research Opportunities},
author = {Zahid Akhtar and Christian Micheloni and Gian Luca Foresti},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943330387&doi=10.1109%2fMSP.2015.116&partnerID=40&md5=5eb4f7f862451d801a47cdf7d6f63a0d},
doi = {10.1109/MSP.2015.116},
year = {2015},
date = {2015-01-01},
journal = {IEEE Security and Privacy},
volume = {13},
number = {5},
pages = {63 – 72},
note = {Cited by: 108},
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pubstate = {published},
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}