Exploiting clustering approaches for image re-ranking

This paper presents the Distance Optimization Algorithm (DOA), a re-ranking method aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) systems. DOA considers an iterative clustering approach based on distances correlation and on the similarity of ranked lists. The algorithm explores the fact that if two images are similar, their distances to other images and therefore their ranked lists should be similar as well. We also describe how DOA can be used to combine different descriptors and then improve the quality of results of CBIR systems. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method, when compared with state-of-the-art approaches.

► Presentation of Distance Optimization Algorithm (DOA), a re-ranking method for CBIR systems. ► Description on how DOA handles distances correlation and similarities of ranked lists for re-ranking. ► Description of how DOA can be applied to combine different descriptors in CBIR systems. ► Evaluation of DOA considering different data sets, descriptors, and baselines.