Title

Fast Query Point Movement Techniques With Relevance Feedback For Content-Based Image Retrieval

Abstract

Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques were designed around query refinement based on relevance feedback, suffer from slow convergence, and do not even guarantee to find intended targets. To address those limitations, we propose several efficient query point movement methods. We theoretically prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. Extensive experiments in simulated and realistic environments 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 employed to improve the effectiveness and efficiency of existing CBIR systems. © Springer-Verlag Berlin Heidelberg 2006.

Publication Date

1-1-2006

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

3896 LNCS

Number of Pages

700-717

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/11687238_42

Socpus ID

33745714816 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33745714816

This document is currently not available here.

Share

COinS