A simple computational model for image retrieval with weighted multifeatures based on orthogonal polynomials and genetic algorithm

This paper proposes a simple and new image retrieval method with weighted multifeature set based on multiresolution 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, directional, perceptual and invariant texture, shape and color features are directly extracted from the subband coefficients. The extracted texture, shape and color features are integrated into linear multifeature set and the significance of each feature in the multifeature 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 multifeature multi feature set using genetic algorithm. Then the obtained optimized weight is multiplied with the corresponding features in the multifeature 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 and Caltech database images. The performance of the proposed method is compared with other existing retrieval methods such as Haar wavelet and Contourlet Transform based retrieval schemes. The proposed method yields high average recall and precision of 92.6% and 71% for Corel database and 90.5% and 72.3% of Caltech database images when compared with other existing methods.