TY - CONF ID - www2009175 UR - http://www2009.eprints.org/175/ A1 - Chi, Mingmin A1 - Zhang, Peiwu A1 - Zhao, Yingbin A1 - Feng, Rui A1 - Xue, Xiangyang Y1 - 2009/04// N2 - General image retrieval is often carried out by a text-based search engine, such as Google Image Search. In this case, natural language queries are used as input to the search engine. Usually, the user queries are quite ambiguous and the returned results are not well-organized as the ranking often done by the popularity of an image. In order to address these problems, we propose to use both textual and visual contents of retrieved images to reRank web retrieved results. In particular, a machine learning technique, a multi-view clustering algorithm is proposed to reorganize the original results provided by the text-based search engine. Preliminary results validate the effectiveness of the proposed framework. TI - Web Image Retrieval ReRanking with Multi-view Clustering SP - 1189 M2 - Madrid, Spain AV - public EP - 1189 T2 - 18th International World Wide Web Conference ER -