Optimizing multimodal reranking for web image search

Authors:
Hao Li;Meng Wang;Zhisheng Li;Zheng-Jun Zha;Jialie Shen
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;Singapore Management University, Singapore, Singapore
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

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Abstract

In this poster, we introduce a web image search reranking approach with exploring multiple modalities. Diff erent 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.