Title

Efficient Target Search With Relevance Feedback For Large Cbir Systems

Keywords

Image retrieval; Query point movement; Relevance feedback; Target search

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. Copyright 2006 ACM.

Publication Date

1-1-2006

Publication Title

Proceedings of the ACM Symposium on Applied Computing

Volume

2

Number of Pages

1393-1397

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1141277.1141598

Socpus ID

33751062654 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33751062654

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