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
Proceedings of IEEE Power Engineering Society General Meeting, 2007.

hal-00692555
https://hal.archives-ouvertes.fr/hal-00692555

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper we propose a hybrid approach using mutual information and multi-objective optimization for feature subset selection problem. The hybrid aspect is due to the sequence of a filter method and a wrapper method in order to take advantages of both. The filter method reduces the exploration space by keeping subsets having good internal properties and the wrapper method chooses among the remaining subsets with a classification performances criterion. In the filter step, the subsets are evaluated in a multi-objective way to ensure diversity within the subsets. The evaluation is based on the mutual information to estimate the dependency between features and classes and the redundancy between features within the same subset. We kept the non-dominated (Pareto optimal) subsets for the second step. In the wrapper step, the selection is made according to the stability of the subsets regarding classification performances during learning stage on a set of classifiers to avoid the specialization of the selected subsets for a given classifiers. The proposed hybrid approach is experimented on a variety of reference data sets and compared to the classical feature selection methods FSDD and mRMR. The resulting algorithm outperforms these algorithms and the computation complexity remains acceptable even if it increases with regards to these two fast selection methods.
Journal of Informatics and Mathematical Sciences

hal-01375982
https://hal.archives-ouvertes.fr/hal-01375982

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
Les écosystèmes forestiers des Caraïbes

hal-00634756
https://hal.archives-ouvertes.fr/hal-00634756

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with texture classification using a multifractal approach. More precisely we analyse the singularity/regularity exponent that compose the textures because they theoretically carry most of the information. The analysis is made using the Legendre spectrum. Then a parameter vector is computed to describe this spectrum in order to classify the textures with an unsupervised k-means classifier. The resulting algorithm is evaluated against a classification directly based on the textures.
CAS Proceedings
GOSIER, France

hal-00634750
https://hal.archives-ouvertes.fr/hal-00634750

Éditeur(s) : HAL CCSD
Résumé : International audience
Les écosystèmes forestiers des Caraïbes

hal-00634757
https://hal.archives-ouvertes.fr/hal-00634757

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
Résumé : International audience
ISSN: 0974875X

hal-01313378
https://hal.archives-ouvertes.fr/hal-01313378

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
ICPRAM
Rome, Italy

hal-01313380
https://hal.archives-ouvertes.fr/hal-01313380

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with image processing. This study takes place in a segmentation process based on texture analysis. We use the multifractal ap-proach to characterize the textures. More precisely we study a particular multi-fractal spectrum called the large deviation spectrum. We consider two statistical methods to numerically compute this spectrum. The resulting spectrum, com-puted by both methods over an image, is a one dimension spectrum. In the scope of this article, we extend these methods in order to obtain a two dimen-sions spectrum which could be assimilated to an image. This 2D spectrum al-lows a local characterization of the image singularities while a 1D spectrum is a global characterization. Moreover, the computation of the spectrum requires the use of a measure. We introduce here a pre processing based on the gradient to improve the measure. We show results on both synthetic and real world images. Finally, we remark that the resulting 2D spectrum is close to the resulting im-age of an edge detection process while edge detection using one dimension spectrum requires post processing methods. This statement will be used for fu-ture works.
SITIS Proceedings
Hammamet, Tunisia

hal-00602273
https://hal.archives-ouvertes.fr/hal-00602273

Éditeur(s) : HAL CCSD Springer
Signal Processing for Image Enhancement and Multimedia Processing

hal-00509497
https://hal.archives-ouvertes.fr/hal-00509497

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
In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results. An unsupervised k-means is used as clustering approach for the texture classification. The algorithm is applied on mosaic image built using IKONOS images and various natural textures from the Brodatz album. The segmentation obtained with our approach gives better results than the application of each spectrum separately.
CIARP Proceedings
Cancun, Mexico

hal-00602268
https://hal.archives-ouvertes.fr/hal-00602268

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

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with texture classification using a multifractal approach. More precisely we analyse the singularity/regularity exponent that compose the textures because they theoretically carry most of the information. The analysis is made using the Legendre spectrum. Then a parameter vector is computed to describe this spectrum in order to classify the textures with an unsupervised k-means classifier. The resulting algorithm is evaluated against a classification directly based on the textures.
ICSP Proceedings
Beijing, China

hal-00602270
https://hal.archives-ouvertes.fr/hal-00602270