Image Retrieval by Local Contrast Patterns and Color

Despite simplicity of the Local binary patterns (LBP) or local edge patterns (LEP) for texture description, they do not always convey complex pattern information. Moreover they are susceptive to various image distortions. Hence we propose a new descriptor called Local Contrast Patterns(LCP), which encode the joint difference distribution of filter responses that can be effectively computed by the higher order directional Gaussian derivatives. Though statistical moments of the filter responses are typical texture features, various complex patterns ( e.g., edges, points, blobs) are well captured by the proposed LCP. Observation shows that anyone of the first few derivatives can produce promising results compared to LBP(or LEP). To extract more improved outcome, two sub-optimal descriptors (LCP1, LCP2) are computed by maximizing local bit frequency and local contrast-ratio. Global RGB color histogram is then combined with the proposed LCP descriptors for color-texture retrieval. Experiments with the grayscale (Brodatz album) and color-texture (MIT VisTex) databases show that our proposed LCP (LCP+RGB) produces 8 % and 2.1 % (1.4 % and 1.9 % ) improved recall rates compared to LBP and LEP (LBP+RGB and LEP+RGB) features. The achievement of the lowest rank ratio, i.e., 2.789 for gray images (1.482 for color images) also indicates the potentiality of the proposed LCP2(LCP2+RGB) feature.