Weighted Multi Feature Based Image Retrieval with Orthogonal Polynomials Model and Genetic Algorithm

Abstract

This paper proposes a new image retrieval method with weighted multi feature in multi resolution enhanced orthogonal polynomials model and genetic algorithm. In the proposed method, initially the orthogonal polynomials model coefficients are computed and reordered into multiresolution subband like structure. Then the statistical and invariant texture and shape features such as mean, standard deviation and moments are directly extracted from the subband coefficients. The extracted texture and shape features are integrated into linear multi feature set and the significance of each feature in the multi feature set is determined by assigning appropriate weight. This paper also proposes a method to compute the optimized weight for each feature in the integrated linear multi feature set using genetic algorithm. Then the obtained optimized weight is multiplied with the corresponding features in the multi feature set and the weighted Manhattan distance metric is used for retrieving similar images. The efficiency of the proposed method is experimented on the standard subset of COREL database images and yields promising results.