Accelerating of Image Retrieval in CBIR System with Relevance Feedback
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Research Article Accelerating of Image Retrieval in CBIR System with Relevance Feedback Goran Zaji´c,1 Nenad Koji´c,1 Vladan Radosavljevi´c,2 Maja Rudinac,1 Stevan Rudinac,3 Nikola Reljin,1 Irini Reljin,1, 3 and Branimir Reljin3 1 College
of Information and Communication Technologies, Belgrade, Serbia and Information Sciences Department, Information Science and Technology Center, Temple University, Philadelphia, PA 19122, USA 3 Digital Image Processing, Telemedicine and Multimedia Laboratory, Faculty of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11000 Belgrade, Serbia 2 Computer
Received 12 September 2006; Revised 22 February 2007; Accepted 29 April 2007 Recommended by Ebroul Izquierdo Content-based image retrieval (CBIR) system with relevance feedback, which uses the algorithm for feature-vector (FV) dimension reduction, is described. Feature-vector reduction (FVR) exploits the clustering of FV components for a given query. Clustering is based on the comparison of magnitudes of FV components of a query. Instead of all FV components describing color, line directions, and texture, only their representative members describing FV clusters are used for retrieval. In this way, the “curse of dimensionality” is bypassed since redundant components of a query FV are rejected. It was shown that about one tenth of total FV components (i.e., the reduction of 90%) is sufficient for retrieval, without significant degradation of accuracy. Consequently, the retrieving process is accelerated. Moreover, even better balancing between color and line/texture features is obtained. The efficiency of FVR CBIR system was tested over TRECVid 2006 and Corel 60 K datasets. Copyright © 2007 Goran Zaji´c et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
The end of the last millennium was characterized by an explosive growth of digital technologies leading to widespread, cheap, but powerful, devices for audio-video data acquisition, processing, storing, and displaying. These new technologies, known as multimedia, have enabled the creation of huge digital multimedia libraries for personal entertainment, professional, and commercial use. Today, all aspects of human life are covered by appropriate digital record. Moreover, global networking through Internet permits us to be a part of a “global village” reaching any point over the Globe and using any available information. New technologies have a strong impact on our daily life and we changed our way of living, working, thinking, and learning. But, surprisingly, a growth of available information produces an opposite effect: more files less benefits. How to find relevant information into the ocean of available data? The effective data storage and management become highly important. There are constant and urgent needs for efficient indexing, searching, browsing, and retrieving of requi
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