Modern Information Retrieval
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Dual-ranking for web image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Actions in stillweb images: visualization, detection and retrieval
WAIM'11 Proceedings of the 12th international conference on Web-age information management
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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.