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|Title:||Optimizing multimodal reranking for web image search|
|Citation:||Li, H.,Wang, M.,Li, Z.,Zha, Z.-J.,Shen, J. (2011). Optimizing multimodal reranking for web image search. SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval : 1119-1120. ScholarBank@NUS Repository. https://doi.org/10.1145/2009916.2010078|
|Abstract:||In this poster, we introduce a web image search reranking approach with exploring multiple modalities. Different from the conventional methods that build graph with one feature set for reranking, our approach integrates multiple feature sets that describe visual content from different aspects. We simultaneously integrate the learning of relevance scores, the weighting of different feature sets, the distance metric and the scaling for each feature set into a unified scheme. Experimental results on a large data set that contains more than 1,100 queries and 1 million images demonstrate the effectiveness of our approach.|
|Source Title:||SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Appears in Collections:||Staff Publications|
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