Using Relevance Feedback in Content-Based Image Metasearch
MetaSeek is an image metasearch engine developed to explore the querying of large, distributed, online visual information systems. The current implementation integrates user feedback into a performance-ranking mechanism. MetaSeek selects and queries the target image search engines according to their success under similar query conditions in previous searches. The current implementation keeps track of each target engine's performance by integrating user feedback for each visual query into a performance database. We begin with a review of the issues in content-based visual query, then describe the current MetaSeek implementation. We present the results of experiments that evaluated the implementation in comparison to a previous version of the system and a baseline engine that randomly selects the individual search engines to query. We conclude by summarizing open issues for future research.