Authors: Rostom Kachouri, Khalifa Djemal, Hichem Maaref
Addresses: IBISC, CNRS 3190, Universite d'Evry Val d'Essonne, 40 rue du Pelvoux, 91020 Evry, France. ' IBISC, CNRS 3190, Universite d'Evry Val d'Essonne, 40 rue du Pelvoux, 91020 Evry, France. ' IBISC, CNRS 3190, Universite d'Evry Val d'Essonne, 40 rue du Pelvoux, 91020 Evry, France
Abstract: Kernel based machine learning such as Support Vector Machines (SVMs) have proven to be powerful for many database classification problems, especially for Content Based Image Retrieval systems (CBIR). Multiple Kernel Learning (MKL) approach was recently proposed to improve kernel based classification results. MKL approach depends essentially on the used kernels and the computation of the optimal weight coefficients. However in case of heterogeneous databases, the complexity to treat and classify images provides great difficultly to define and determine optimal kernel weights. We propose in this paper an original kernel weighting method, which is intended for Multiple Kernel based SVM classification. Depending on the relevance of kernel training rates, the proposed method allows us to ensure better classification accuracy and significantly less computation time.
Keywords: CBIR systems; content based image retrieval; heterogeneous image recognition; SVM; support vector machines; multiple kernel learning; MKL; training rate; kernel weighting; machine learning; kernel based classification.
International Journal of Signal and Imaging Systems Engineering, 2011 Vol.4 No.2, pp.60 - 70
Accepted: 04 Mar 2011
Published online: 27 Jul 2011 *