<XML><RECORDS><RECORD><REFERENCE_TYPE>3</REFERENCE_TYPE><REFNUM>7782</REFNUM><AUTHORS><AUTHOR>Urban,J.</AUTHOR><AUTHOR>Jose,J.M.</AUTHOR></AUTHORS><YEAR>2004</YEAR><TITLE>Evidence Combination for Multi-Point Query Learning in Content-Based Image Retrieval</TITLE><PLACE_PUBLISHED>Proc. of the IEEE Sixth International Symposium on Multimedia Software Engineering (MSE 2004)</PLACE_PUBLISHED><PUBLISHER>IEEE Computer Society Press</PUBLISHER><PAGES>583-586</PAGES><ISBN>0-7695-2217-3</ISBN><LABEL>Urban:2004:7782</LABEL><KEYWORDS><KEYWORD>relevance feedback; multi-point query; content-based image retrieval; evidence combination; evaluation</KEYWORD></KEYWORDS<ABSTRACT> In Multi-Point Query Learning a number of query representatives are selected based on the positive feedback samples. The similarity score to a multi-point query is obtained from merging the individual scores. In this paper, we investigate three different combination strategies and present a comparative evaluation of their performance. Results show that the performance of multi-point queries relies heavily on the right choice of settings for the fusion. Unlike previous results, suggesting that multi-point queries generally perform better than a single query representation, our evaluation results do not allow such an overall conclusion. Instead our study points to the type of queries for which query expansion is better suited than a single query, and vice versa. </ABSTRACT></RECORD></RECORDS></XML>