Abstract
In this paper, a new feature descriptor, named correlated primary visual texton histogram features (CPV-THF), for image retrieval is proposed. CPV-THF integrates the visual content and semantic information of the image by finding correlations among the colour, texture orientation, intensity, and local spatial structure information of an image. Based on texton theory, box-shaped structural elements are designed for image texture analysis. The colour, texture orientation, and intensity feature histograms that are proposed in CPV-THF are represented by correlated attributes of the co-occurrence matrix. The performance of the proposed descriptor is compared with those of state-of-the-art texture, colour, shape, and local-pattern-based CBIR descriptors. Experiments are conducted on multiple standard natural image datasets, namely, corel1k, corel5k, and corel10k. Experimental results indicate that the proposed descriptor outperforms various state-of-the-art descriptors, such as GLCM, MTH, MSD, MS-LSP, SED, STH, and MTSD.