Accelerating of Image Retrieval in CBIR System with Relevance FeedbackReport as inadecuate




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EURASIP Journal on Advances in Signal Processing

, 2007:062678

Knowledge-Assisted Media Analysis for Interactive Multimedia Applications

Abstract

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.

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Author: Goran Zajić - Nenad Kojić - Vladan Radosavljević - Maja Rudinac - Stevan Rudinac - Nikola Reljin - Irini Reljin - Branim

Source: https://link.springer.com/







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