document: Web image retrieval reranking with multi-view clustering 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 .
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