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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Accelerating of Image Retrieval in CBIR System with Relevance Feedback | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 62678 13 pages doi 10.1155 2007 62678 Research Article Accelerating of Image Retrieval in CBIR System with Relevance Feedback Goran Zajic 1 Nenad Kojic 1 Vladan Radosavljevic 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 2 Computer 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 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 Zajic et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in .
