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Heterogeneous Image Retrieval System based on Features Extraction and SVM Classifier

Image databases represent increasingly important volume of information, so it is judicious to develop powerful systems to handle the images, index them, classify them to reach them quickly in these large image databases. In this paper, we propose an heterogeneous image retrieval system based on feature extraction and Support vector machines (SVM) classifier. For an heterogeneous image database, first of all we extract several feature kinds such as color descriptor, shape descriptor, and texture descriptor. Afterwards we improve the description of these features, by some original methods. Finally we apply an SVM classifier to classify the consequent index database. For evaluation purposes, using precision/recall curves on an heterogeneous image database, we looked for a comparison of the proposed image retrieval system with an other Content-based image retrieval (CBIR) which is QUadtree-based Index for image retrieval and Pattern search (QUIP-tree). The obtained results show that the proposed system provides good accuracy recognition, and it prove more better than QUIP-tree method.