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CIVR
2010
Springer

Co-reranking by mutual reinforcement for image search

10 years 29 days ago
Co-reranking by mutual reinforcement for image search
Most existing reranking approaches to image search focus solely on mining "visual" cues within the initial search results. However, the visual information cannot always provide enough guidance to the reranking process. For example, different images with similar appearance may not always present the same relevant information to the query. Observing that multi-modality cues carry complementary relevant information, we propose the idea of co-reranking for image search, by jointly exploring the visual and textual information. Co-reranking couples two random walks, while reinforcing the mutual exchange and propagation of information relevancy across different modalities. The mutual reinforcement is iteratively updated to constrain information exchange during random walk. As a result, the visual and textual reranking can take advantage of more reliable information from each other after every iteration. Experiment results on a real-world dataset (MSRA-MM) collected from Bing image ...
Ting Yao, Tao Mei, Chong-Wah Ngo
Added 29 Sep 2010
Updated 29 Sep 2010
Type Conference
Year 2010
Where CIVR
Authors Ting Yao, Tao Mei, Chong-Wah Ngo
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