Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Allili, M. S.; Ziou, D.
Likelihood-based feature relevance for figure-ground segmentation in images and videos Article de journal
Dans: Neurocomputing, vol. 167, p. 658–670, 2015, ISSN: 09252312, (Publisher: Elsevier).
Résumé | Liens | BibTeX | Étiquettes: accuracy, algorithm, article, calculation, Feature relevance, Figure-ground segmentations, Gaussian mixture model (GMMs), Image analysis, Image Enhancement, image quality, Image segmentation, Level Set, linear system, mathematical analysis, mathematical model, Negative examples, priority journal, Video cameras, videorecording
@article{allili_likelihood-based_2015,
title = {Likelihood-based feature relevance for figure-ground segmentation in images and videos},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952631642&doi=10.1016%2fj.neucom.2015.04.015&partnerID=40&md5=833948d0784e0dc42c2245b9343971dd},
doi = {10.1016/j.neucom.2015.04.015},
issn = {09252312},
year = {2015},
date = {2015-01-01},
journal = {Neurocomputing},
volume = {167},
pages = {658–670},
abstract = {We propose an efficient method for image/video figure-ground segmentation using feature relevance (FR) and active contours. Given a set of positive and negative examples of a specific foreground (an object of interest (OOI) in an image or a tracked objet in a video), we first learn the foreground distribution model and its characteristic features that best discriminate it from its contextual background. For this goal, an objective function based on feature likelihood ratio is proposed for supervised FR computation. FR is then incorporated in foreground segmentation of new images and videos using level sets and energy minimization. We show the effectiveness of our approach on several examples of image/video figure-ground segmentation. © 2015 Elsevier B.V.},
note = {Publisher: Elsevier},
keywords = {accuracy, algorithm, article, calculation, Feature relevance, Figure-ground segmentations, Gaussian mixture model (GMMs), Image analysis, Image Enhancement, image quality, Image segmentation, Level Set, linear system, mathematical analysis, mathematical model, Negative examples, priority journal, Video cameras, videorecording},
pubstate = {published},
tppubtype = {article}
}
We propose an efficient method for image/video figure-ground segmentation using feature relevance (FR) and active contours. Given a set of positive and negative examples of a specific foreground (an object of interest (OOI) in an image or a tracked objet in a video), we first learn the foreground distribution model and its characteristic features that best discriminate it from its contextual background. For this goal, an objective function based on feature likelihood ratio is proposed for supervised FR computation. FR is then incorporated in foreground segmentation of new images and videos using level sets and energy minimization. We show the effectiveness of our approach on several examples of image/video figure-ground segmentation. © 2015 Elsevier B.V.
2.
Allili, M. S.; Ziou, D.
Using feature selection for object segmentation and tracking Article d'actes
Dans: Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, p. 191–198, Montreal, QC, 2007, ISBN: 0-7695-2786-8 978-0-7695-2786-4.
Résumé | Liens | BibTeX | Étiquettes: Active contours, Algorithms, Feature extraction, Feature relevance, Image segmentation, Maximum likelihood, Mixture models, Negative examples, Object of interest (OOI), Optimization, Target tracking
@inproceedings{allili_using_2007,
title = {Using feature selection for object segmentation and tracking},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548781938&doi=10.1109%2fCRV.2007.67&partnerID=40&md5=3fb26f3fcc7a6f55f705255758fef582},
doi = {10.1109/CRV.2007.67},
isbn = {0-7695-2786-8 978-0-7695-2786-4},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},
pages = {191–198},
address = {Montreal, QC},
abstract = {Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance was used. © 2007 IEEE.},
keywords = {Active contours, Algorithms, Feature extraction, Feature relevance, Image segmentation, Maximum likelihood, Mixture models, Negative examples, Object of interest (OOI), Optimization, Target tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance was used. © 2007 IEEE.