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. Categories and Subject Descriptors H.3.3 [Information Systems]: INFORMATION STORAGE AND RETRIEVAL--Information Search and Retrieval General Terms Algorithms, Design Keywords Web image retrieval, reRanking, multi-view clustering

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Mingmin Chi, Peiwu Zhang, Yingbin Zhao, Rui Feng, Xiangyang Xue
• Keywords: Google Image Search, Internet Technology, Multi-view Clustering Algorithm, Text-based Search Engine, WWW 2009