On integrating re-ranking and rank list fusion techniques for image retrieval

Abstract

This paper aims to unify image re-ranking and rank aggregation strategies to enhance the retrieval precision of content-based image retrieval (CBIR) systems. In general, CBIR systems are concerned with the retrieval of a set of relevant images from large repositories in response to a submitted query. The primary objective of CBIR systems is the exact ordering of database images in accordance with the presented query. To this end, we present a novel image re-ranking scheme for reordering the initial search results returned by multiple retrieval models and an efficient rank list fusion scheme to combine these refined retrieval results to achieve better performance. The re-ranking algorithm introduced in this work utilizes distance correlation coefficient to refine the search result generated by a given retrieval model. It involves two-step clustering of the initial retrieval list followed by an adaptive procedure for updating the similarity scores among images based on the created clusters. Similarly, the Particle Swarm Optimization-based similarity score fusion framework presented in this work optimally combines the retrieval results generated by multiple CBIR systems. The proposed approach is evaluated on various retrieval tasks using state-of-the-art low-level and high-level descriptors. Experimental results show that our model can significantly enhance the overall effectiveness of CBIR systems.