Multiple kernel weighting based SVM for heterogeneous image recognition system
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.