A hierarchical approach based CBIR scheme using shape, texture, and color for accelerating retrieval process

Abstract

In the traditional content-based image retrieval (CBIR) framework, images are retrieved based on the combined primitive features. However, such a kind of fusion is always not effective as one feature may overshadow other image feature. To overcome this issue, in this particular paper, we have suggested a hierarchical framework where features like color, texture, and shape are considered in a single hierarchy only. The main concern of the hierarchical system is the proper selection of the order of retrieving visual features. So, semantic-based image retrieval has been carried out. Here, at the first level, the salience map-based region of interest has been identified, and then edge histogram descriptor-based shape features are incorporated. In the second hierarchy, we have proposed a novel directional texture feature extraction based on the Tamura features’ directionality. Further, color is considered another primitive feature of an image, but human visual perception is not sensitive to each color. The image can be visualized by the salient colors, and in this work, we have developed a color image quantization-based approach. Now, to validate the system, extensive experimental results and its comparison with its contemporaries through Corel-1 K, GHIM-10 K, Olivia-2688, and Produce-1400 databases have been carried out.