Visual search reranking via adaptive particle swarm optimization

Publication Type:
Journal Article
Citation:
Pattern Recognition, 2011, 44 (8), pp. 1811 - 1820
Issue Date:
2011-08-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011001228OK.pdf1.03 MB
Adobe PDF
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 20062007 benchmarks and show significant and consistent improvements over state-of-the-art works. © 2011 Elsevier Ltd. All rights reserved.
Please use this identifier to cite or link to this item: