Local neighbourhood-based robust colour occurrence descriptor for colour image retrieval

Content-based image retrieval (CBIR) is demanding accurate with efficient retrieval approaches to index and retrieve the most similar images from the huge image databases. This study introduces a novel local neighbourhood-based robust colour occurrence descriptor (LCOD) to encode the colour information present in the local structure of the image. The colour information is processed in two steps: first, the number of colours is reduced into a less number of shades by quantising the red-green-blue colour space; second, the reduced colour shade information of the local neighbourhood is used to compute the descriptor. A local colour occurrence binary pattern is generated for each pixel of the image by representing each reduced colour shade occurrence in its local neighbourhood using a binary pattern. The descriptor is constructed by summing the local colour occurrence binary patterns of all the pixels in the image. LCOD is tested over the natural and colour texture databases for CBIR and experimental results suggest that LCOD outperforms other state-of-the-art descriptors. The performance of the proposed descriptor is promising in the case of illumination difference, rotation and scaling also and it can be effectively used for accurate image retrieval under various image transformations.