Abstract
Visual search reranking involves an optimization process that uses visual content to recover the ''genuine'' ranking list from the helpful but noisy one generated by textual search. This paper presents an evolutionary approach, called Adaptive Particle Swarm Optimization (APSO), for unsupervised visual search reranking. The proposed approach incorporates the visual consistency regularization and the ranking list distance. In addition, to address the problem that existing list distance fails to capture the genuine disagreement between two ranking lists, we propose a numerical ranking list distance. Furthermore, the parameters in APSO are self-tuned adaptively according to the fitness values of the particles to avoid being trapped in local optima. We conduct extensive experiments on automatic search task over TRECVID 2006-2007 benchmarks and show significant and consistent improvements over state-of-the-art works.
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