JACIII Vol.14 No.1 pp. 34-38
doi: 10.20965/jaciii.2010.p0034


Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method

M. Rahmat Widyanto* and Tatik Maftukhah**

*Faculty of Computer Science, University of Indonesia Depok Campus, Depok 16424, West Java, Indonesia

**Indonesian Institute of Science Puspiptek, Serpong, Banten 15314, Indonesia

April 23, 2009
July 23, 2009
January 20, 2010
fuzzy, relevance, feedback, QVM, CBIR

Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are: image similarity, output image, computation of membership value, feedback computation, and feedback result. Experiments used QVM method for six relevance levels. Fuzzy relevance feedback using QVM method gives higher precision value than conventional relevance feedback method. Experimental results show that the precision value improved by 28.56% and recall value improved 3.2% of conventional relevance feedback. That indicated performance Image Retrieval System can be improved by fuzzy relevance feedback using QVM method.

Cite this article as:
M. Rahmat Widyanto and Tatik Maftukhah, “Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.1, pp. 34-38, 2010.
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Last updated on Mar. 05, 2021