Efficient target search with relevance feedback for large CBIR systems

Authors:
Danzhou Liu;Kien A. Hua;Khanh Vu;Ning Yu
Affiliations:
University of Central Florida, Orlando, Florida;University of Central Florida, Orlando, Florida;University of Central Florida, Orlando, Florida;University of Central Florida, Orlando, Florida
Venue:
Proceedings of the 2006 ACM symposium on Applied computing
Year:
2006

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Abstract

Recent content-based image retrieval (CBIR) techniques were designed around query refinement based on relevance feedback. They suffer from slow convergence, high disk I/O, and do not even guarantee to find intended targets. In this paper, we identify the cause of these problems and propose several efficient target search methods to address these drawbacks. Our complexity analysis shows that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We evaluated our techniques on large datasets in simulated and realistic environments. The results show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be used to improve the effectiveness of existing CBIR systems with relevance feedback.