Taking advantage of multi-regions-based diagonal texture structure descriptor for image retrieval

With the rapid development of information technology, the capacity of Web images database becomes larger and larger. How to quickly and effectively find the desired images in Web image databases becomes the challenge needed to resolve with high priority. In this paper a novel diagonal texture structure descriptor (DTSD) is proposed, and a new framework considering hue, saturation and value components is utilized for image retrieval. In specific, we firstly use Otsu algorithm to segment image into foreground and background, and the features of multi-regions are respectively considered. That is, we present the contents of these multi-regions distinctively to reduce the influence of each other, which would perform hierarchical feature description and realize more accurate content match for image retrieval. In this study, to simulate the characteristic of human eyes for perceiving colors, hue and saturation components are quantized into various bins which can obtain more detailed description for color difference. Meanwhile, DTSD is extracted based on value component to represent the edge information as the feature of receptive field. Such a method can improve the spatial resolution ability of the descriptor, and identify finer structure of an image. Moreover, histogram with respect to these three components, i.e., hue, saturation and value, is utilized to generate the feature vector of an image. We carry out the experiments on benchmark Corel and UCID image datasets, and the extensive experimental results demonstrate that our method achieves better performance in comparison with state of the art image retrieval algorithms. The proposed method is very promising, which can provide more accurate retrieved results on the basis of color & texture descriptions in multi-regions, and further enhances the performance of the intelligent image retrieval system.