A hierarchical CBIR framework using adaptive tetrolet transform and novel histograms from color and shape features

Abstract

Content-Based Image Retrieval (CBIR) systems retrieve the most analogous images from the image database with respect to a given query image based on the texture, shape, and/or color image features. These three image features can be used alone for the image retrieval or also can be used together for the retrieval purpose. In hierarchical CBIR system, three image features are extracted in proper order to discard the irrelevant images in each hierarchy level for reducing the image search space. In this paper, the authors have proposed a three-level hierarchical CBIR system/framework where, each level of the hierarchy uses either texture, shape or color image features to reduce the size of the image database by discarding the irrelevant images and at final level of the hierarchy, it will extract the most analogous images from the reduced image database. We have used adaptive tetrolet transform to extract the texture features from the regions of interest of the images. To extract the shape features of the image, a novel edge joint histogram has been proposed which uses the orientation of the edge pixels and their distance from the origin together to create a novel joint histogram. For color feature extraction, another color channel correlation histogram has been introduced. The order of the three different feature extraction processes on each level of the hierarchy is not rigid because it is difficult to predict the proper order for the highest retrieval. In the experiment, we have considered all possible order of the texture, shape and color features for image retrieval process. The retrieval experiments have been carried out in six different types of standard image databases and results show that the performance of proposed CBIR system has been increased significantly as compared to the other state-of-arts CBIR systems.