Éditeur(s) : HAL CCSD
Résumé : International audience
This paper shows the information criteria (IC) performances in feature selection framework. Feature selection aims to select a representative subset among a wide set of features. We apply this approach to classify an hand segmented image. The performance is tested using various feature selection schemes (SFS, SBS, SFFS and SBFS) to select the candidate subsets. The accuracy of the approach is based on a good quality of the joint probability density approximation of the combined features. They are obtained using histogram optimized thanks to the adaptive arithmetic coding principles. Our approach is tested on different reference data. The subsets quality is evaluated using correct classification rate computed on multiple classifiers. Results show stability and convergence properties of this tool and its ability to select representative subsets (in the sense that the subset of feature is a good characterization of the classes in which the data belong). Information Criteria could be used for feature selection as a good alternative to other criteria.
CISP Proceedings
Shangay, China

hal-00634749
https://hal.archives-ouvertes.fr/hal-00634749

Éditeur(s) : HAL CCSD
Résumé : International audience
This work proposes an original approach using mutual information and Pareto curve jointly for feature selection. Mutual information is used to estimate dependency criterion between features and classes and redundancy criterion between features taken two by two. Unlike some studies, these criteria are used simultaneously to compute Pareto curve and determine the optimal feature sets. This approach is tested on more reference data. Several clustering algorithms are used to compute classification accuracy. The obtained results show the importance of our tools and its ability to select the best feature sets that give the better description.
GRETSI Proceedings
Bordeaux, France

hal-00602260
https://hal.archives-ouvertes.fr/hal-00602260

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper introduces the information criteria in feature selection framework. Information criteria are integrated in feature selection scheme to select subset candidates. The accuracy of the proposed approach is based on the quality of the probability density approximations of these features. They are obtained using histograms optimized thanks to the adaptive arithmetic coding principles. Tests on simulated data and references are made. Multiple classifiers are used. The correct classification rate shows, the importance of this tools and its ability to select a best subsets. Generally, the subsets produce a good characterization of classes which the data belong.
TAIMA Proceedings

hal-00634754
https://hal.archives-ouvertes.fr/hal-00634754

Éditeur(s) : HAL CCSD Lavoisier
Résumé : National audience
ISSN: 0765-0019

hal-00509496
https://hal.archives-ouvertes.fr/hal-00509496

Éditeur(s) : HAL CCSD
Résumé : International audience
SITIS Proceeedings

hal-00509493
https://hal.archives-ouvertes.fr/hal-00509493

Éditeur(s) : HAL CCSD
Résumé : International audience
The fusion of regions from segmentation is often a required step in order to analyze objects in the scene. It allows to group under a same closure property the partitions of an image, which denote the same object. In this way, several studies have been made. Most of the algorithms are based on global information to evaluate the relevance of region combinations and fusion. In this paper, we propose a merging process based on Gestalt vision's theory. The particularity of our method is to preserve photographer's region of interest thanks to the definition of a new criterion based on Di Zenzo gradient. Thus, region fusion preserves photographer's relevant objects of the scene. The performance of the proposed method is evaluated using various natural images, including Berkeley image database and its accuracy is shown.
Color and Imaging Conference
San Jose, CA, United States

hal-00637144
https://hal.archives-ouvertes.fr/hal-00637144

Éditeur(s) : HAL CCSD
Résumé : International audience
Proceedings of VIPImage
Porto, Portugal

hal-00509491
https://hal.archives-ouvertes.fr/hal-00509491

Éditeur(s) : HAL CCSD
Résumé : International audience
IEEE SPIE Remote Sensing Proceedings
Cardiff, United Kingdom

hal-00509494
https://hal.archives-ouvertes.fr/hal-00509494

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with a comparison of different colour space in order to improve high resolution images classification. The background of this study is the measure of the agriculture impact on the environment in islander context. Biodiversity is particularly sensitive and relevant in such areas and the follow-up of the forest front is a way to ensure its preservation. Very high resolution satellite images are used such as QuickBird and IKONOS scenes. In order to segment the images into forest and agriculture areas, we characterize both ground covers with colour and texture features. A classical unsupervised classifier is then used to obtain labelled areas. As features are computed on coloured images, we can wonder if the colour space choice is relevant. This study has been made considering more than fourteen colour spaces (RGB, YUV, Lab, YIQ, YCrCs, XYZ, CMY, LMS, HSL, KLT, IHS, I1I2I3, HSV, HSI, etc.) and shows the visual and quantitative superiority of IHS on all others. For conciseness reasons, results only show RGB, I1I2I3 and IHS colour spaces.
SPIE Remote Sensing Proceedings
Cardiff, United Kingdom

hal-00602277
https://hal.archives-ouvertes.fr/hal-00602277