Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Video search re-ranking via multi-graph propagation
Proceedings of the 15th international conference on Multimedia
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Optimizing video search reranking via minimum incremental information loss
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Multigraph-based query-independent learning for video search
IEEE Transactions on Circuits and Systems for Video Technology
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization
IEEE Transactions on Multimedia
Optimizing Visual Search Reranking via Pairwise Learning
IEEE Transactions on Multimedia
A heterogenous automatic feedback semi-supervised method for image reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.