Histogram refinement for texture descriptor based image retrieval

Texture descriptors such as local binary patterns (LBP) have been successfully employed for feature extraction in image retrieval algorithms because of their high discriminating ability and computational efficiency. In this paper, we propose histogram feature refinement methods for enhancing performance of texture descriptor based content-based image retrieval (CBIR) systems. In the proposed approach for histogram refinement, each pixel in the query and database images is classified into one of the two categories based on the analysis of pixel values in its neighborhood. Local patterns corresponding to two sets of pixels are used to generate two histogram features for each image, effectively resulting in splitting of the original global histogram of texture descriptors into two based on the category of each pixel. Resulting histograms are then concatenated to form a single histogram feature. This study also explores three hybrid frameworks for histogram refinement in CBIR systems. Comparison of histogram features corresponding to query and database images are performed using the relative l1 distance metric. Performance evaluation on three publicly available benchmark image databases namely, GHIM 10000, COREL 1000 database, and Brodatz texture database shows that performances of existing texture descriptor based approaches improve considerably when the proposed histogram feature refinement is incorporated. Specifically, the average precision rate is improved by 6.02%, 5.69%, 4.79%, and 4.21% for LBP, local derivative pattern (LDP), local ternary pattern (LTP), and local tetra pattern (LTrP) descriptors, respectively on GHIM 10000 database. The proposed histogram refinement approaches also provide performance improvement for other texture descriptors considered in this study.