Image retrieval based on gradient-structures histogram

Abstract

Color perception and orientation selection are very important mechanisms of the human brain that have close relationships with feature extraction and representation. However, extracting low-level features by mimicking these mechanisms remains challenging. To address this problem, we present the gradient-structures histogram as a novel method of content-based image retrieval (CBIR). Its main highlights are: (1) a novel and easy-to-calculate local structure detector, the gradient-structures, which simulates the orientation selection mechanism based on the opponent-color space and connects it with low-level features, (2) a novel discriminative representation method that describes color, intensity and orientation features. It is convenient, as it does not require weight coefficients for color, intensity and orientation. (3) The proposed representation method has the advantages of being histogram-based and having the power to discriminate spatial layout, color and edge cues. The proposed method provides efficient CBIR performance, as demonstrated by comparative experiments in which it significantly outperformed some state-of-the-art methods, including the Bow method, local binary pattern histogram, perceptual uniform descriptor, color volume histogram, color difference histogram, some improved LBP methods and the Tree2Vector method in terms of precision/recall and AUC metrics.