<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>31</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">El Maia, H</style></author><author><style face="normal" font="default" size="100%">Hammouch, A</style></author><author><style face="normal" font="default" size="100%">Aboutajdine, D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Color-texture analysis by mutual information for multispectral image classification</style></title><secondary-title><style face="normal" font="default" size="100%">Communications, Computers and Signal Processing, 2009. PacRim 2009. IEEE Pacific Rim Conference on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">co-occurrence matrix</style></keyword><keyword><style  face="normal" font="default" size="100%">feature extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">forest areas</style></keyword><keyword><style  face="normal" font="default" size="100%">hybrid color texture space</style></keyword><keyword><style  face="normal" font="default" size="100%">image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Image color analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">image colour analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Image databases</style></keyword><keyword><style  face="normal" font="default" size="100%">image texture</style></keyword><keyword><style  face="normal" font="default" size="100%">Information analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Morocco</style></keyword><keyword><style  face="normal" font="default" size="100%">multispectral image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Multispectral imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">mutual information</style></keyword><keyword><style  face="normal" font="default" size="100%">Rabat</style></keyword><keyword><style  face="normal" font="default" size="100%">RGB color space</style></keyword><keyword><style  face="normal" font="default" size="100%">Spatial databases</style></keyword><keyword><style  face="normal" font="default" size="100%">SPOT HRV (XS)</style></keyword><keyword><style  face="normal" font="default" size="100%">Support vector machine classification</style></keyword><keyword><style  face="normal" font="default" size="100%">support vector machines</style></keyword><keyword><style  face="normal" font="default" size="100%">support vectors machine (citation)</style></keyword><keyword><style  face="normal" font="default" size="100%">VisTex database</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">359-364</style></pages><isbn><style face="normal" font="default" size="100%">VO -</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we propose a new approach for the construction of a hybrid color-texture space by using mutual information. Feature extraction is done by the co-occurrence matrix with SVM (support vectors machine) as a classifier. We apply our approach to the VisTex database and to the classification of a SPOT HRV (XS) image representing two forest areas in the region of Rabat in Morocco. We compare the result of classification obtained in this hybrid space with the one in the RGB color space.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>3</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerroum, M A</style></author><author><style face="normal" font="default" size="100%">Hammouch, A</style></author><author><style face="normal" font="default" size="100%">Aboutajdine, D</style></author><author><style face="normal" font="default" size="100%">Bellaachia, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using the maximum Mutual Information criterion to textural Feature Selection for satellite image classification</style></title><secondary-title><style face="normal" font="default" size="100%">Computers and Communications, 2008. ISCC 2008. IEEE Symposium on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cooccurrence Matrix</style></keyword><keyword><style  face="normal" font="default" size="100%">LDA</style></keyword><keyword><style  face="normal" font="default" size="100%">mutual information</style></keyword><keyword><style  face="normal" font="default" size="100%">PCA</style></keyword><keyword><style  face="normal" font="default" size="100%">Satellite Image Classi- fication</style></keyword><keyword><style  face="normal" font="default" size="100%">SVM</style></keyword><keyword><style  face="normal" font="default" size="100%">Textural Feature Selection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1005-1009</style></pages><isbn><style face="normal" font="default" size="100%">1530-1346 VO -</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents and evaluates the use of the maximum mutual information criterion to textural feature selection for satellite image classification. Our approach is based on a recent work of Mutual Information Feature Selector Algorithm. The effectiveness of the proposed approach is evaluated on real data. In fact, the textural features are extracted using the cooccurrence matrix from two forest zones of SPOT HRV(XS) image in the region of Rabat, Morocco. The experimental tests of this study prove that the proposed approach gives a better performance for satellite image classification than classical methods such as principal components analysis (PCA) and linear discriminant analysis (LDA). The classifier used in this work is the support vectors machine (SVM).</style></abstract></record></records></xml>