Linked Relevance Feedback for the ImageCLEF Photo Task

In this paper we will describe Berkeley’s approach to the ImageCLEFphoto task for CLEF 2007. Once again (as in ImageCLEFphoto for CLEF 2006) we used entirely text-based methods for retrieval. For some runs this year, however, we exploited the basic similarity of the topics and database from 2006 to acquire the metadata descriptions of the “example images” in the 2007 queries, and used that metadata to expand the query content for each topic. The results speak for themselves: use of what amounts to relevance feedback based on image metadata is much more effective than use of unexpanded queries, and even provides a method of cross-language retrieval for unknown languages when parallel topics and example images can be established. We submitted 19 runs for ImageCLEFphoto this year, of which 8 where monolingual English, German and Spanish, and the remaining 11 where bilingual from various languages to English, German and Spanish.