Enhancing relevance re-ranking using nature-inspired meta-heuristic optimization algorithms
Amel Ksibi, Anis Ben Ammar, Chokri Ben Amar · 2014
Over the last years, relevance re-ranking has been an attractive research, aiming to re-order the initial image search result list by which relevant ones should be at the top ranking list and irrelevant ones should be pruned. In this paper, we propose to explore two population-based meta-heuristic algorithms, which are Particle Swarm optimization(PSO), and Cuckoo search(CS), in order to solve the relevance re-ranking problem as a constrained regularisation framework. By doing so, we define two reranking processes, refereed as APSO-Rank and CS-Rank that converge to the optimal ranked list. Results are further provided to demonstrate the effectiveness and performance of these two reranking processes.