Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Ouyed, O.; Allili, M. S.
Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition Journal Article
In: Expert Systems with Applications, vol. 148, 2020, ISSN: 09574174, (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: Arts computing, Computationally efficient, Gradient descent, Gradient methods, Group sparsities, Group-of-features relevance, Human interaction recognition, Multinomial kernels, regression analysis, Relevance weights, State-of-art methods
@article{ouyed_group–features_2020,
title = {Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition},
author = {O. Ouyed and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078662750&doi=10.1016%2fj.eswa.2020.113247&partnerID=40&md5=8738cfe1a8a6ded1d5f247b8c62de724},
doi = {10.1016/j.eswa.2020.113247},
issn = {09574174},
year = {2020},
date = {2020-01-01},
journal = {Expert Systems with Applications},
volume = {148},
abstract = {We propose an approach for human interaction recognition (HIR) in videos using multinomial kernel logistic regression with group-of-features relevance (GFR-MKLR). Our approach couples kernel and group sparsity modelling to ensure highly precise interaction classification. The group structure in GFR-MKLR is chosen to reflect a representation of interactions at the level of gestures, which ensures more robustness to intra-class variability due to occlusions and changes in subject appearance, body size and viewpoint. The groups consist of motion features extracted from tracking interacting persons joints over time. We encode group sparsity in GFR-MKLR through relevance weights reflecting each group (gesture) discrimination capability between different interaction categories. These weights are automatically estimated during GFR-MKLR training using gradient descent minimisation. Our model is computationally efficient and can be trained on a small training dataset while maintaining a good generalization and interpretation capabilities. Experiments on the well-known UT-Interaction dataset have demonstrated the performance of our approach by comparison with state-of-art methods. © 2020 Elsevier Ltd},
note = {Publisher: Elsevier Ltd},
keywords = {Arts computing, Computationally efficient, Gradient descent, Gradient methods, Group sparsities, Group-of-features relevance, Human interaction recognition, Multinomial kernels, regression analysis, Relevance weights, State-of-art methods},
pubstate = {published},
tppubtype = {article}
}
We propose an approach for human interaction recognition (HIR) in videos using multinomial kernel logistic regression with group-of-features relevance (GFR-MKLR). Our approach couples kernel and group sparsity modelling to ensure highly precise interaction classification. The group structure in GFR-MKLR is chosen to reflect a representation of interactions at the level of gestures, which ensures more robustness to intra-class variability due to occlusions and changes in subject appearance, body size and viewpoint. The groups consist of motion features extracted from tracking interacting persons joints over time. We encode group sparsity in GFR-MKLR through relevance weights reflecting each group (gesture) discrimination capability between different interaction categories. These weights are automatically estimated during GFR-MKLR training using gradient descent minimisation. Our model is computationally efficient and can be trained on a small training dataset while maintaining a good generalization and interpretation capabilities. Experiments on the well-known UT-Interaction dataset have demonstrated the performance of our approach by comparison with state-of-art methods. © 2020 Elsevier Ltd
2.
Ouyed, O.; Allili, M. S.
Recognizing human interactions using group feature relevance in multinomial kernel logistic regression Proceedings Article
In: K., Rus V. Brawner (Ed.): Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018, pp. 541–545, AAAI press, 2018, ISBN: 978-1-57735-796-4.
Abstract | Links | BibTeX | Tags: Art methods, Artificial intelligence, Feature relevance, Group sparsities, Human interactions, Image features, Kernel logistic regression, Multinomial kernels, regression analysis, Sparse models
@inproceedings{ouyed_recognizing_2018,
title = {Recognizing human interactions using group feature relevance in multinomial kernel logistic regression},
author = {O. Ouyed and M. S. Allili},
editor = {Rus V. Brawner K.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067977954&partnerID=40&md5=1c8d720de570fc565bca3741c107bc9a},
isbn = {978-1-57735-796-4},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018},
pages = {541–545},
publisher = {AAAI press},
abstract = {We propose a supervised approach incorporating group feature sparsity in multi-class kernel logistic regression (GFR-MKLR). The need for group sparsity arises in several practical situations where a subset of a set of factors can explain a predicted variable and each factor consists of a group of variables. We apply our approach for predicting human interactions based on body parts motion (e.g., hands, legs, head, etc.) where image features are organised in groups corresponding to body parts. Our approach, leads to sparse models by assigning weights to groups of features having the highest discrimination between different types of interactions. Experiments conducted on the UT-Interaction dataset have demonstrated the performance of our method with regard to stat-of-art methods. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.},
keywords = {Art methods, Artificial intelligence, Feature relevance, Group sparsities, Human interactions, Image features, Kernel logistic regression, Multinomial kernels, regression analysis, Sparse models},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a supervised approach incorporating group feature sparsity in multi-class kernel logistic regression (GFR-MKLR). The need for group sparsity arises in several practical situations where a subset of a set of factors can explain a predicted variable and each factor consists of a group of variables. We apply our approach for predicting human interactions based on body parts motion (e.g., hands, legs, head, etc.) where image features are organised in groups corresponding to body parts. Our approach, leads to sparse models by assigning weights to groups of features having the highest discrimination between different types of interactions. Experiments conducted on the UT-Interaction dataset have demonstrated the performance of our method with regard to stat-of-art methods. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.