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
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.
-  F. Long, H. Zhang, and D. D. Feng, “Fundamentals of Content-Based Image Retrieval,” unpublished.
-  R. S. Torres and A. X. Falcao, “Content-Based Image Retrieval: Theory and Application,” RITA, Vol.XIII, No.2, 2006.
-  J. Eakins and M. Graham, “Content-based Image Retrieval,” JISC Technology Application, University of Nothumbria, October, 1999.
-  A. D. Grossman and O. Frieder, “Information Retrieval, Algorithms and Heuristics,” Springer, 2004.
-  S. Deb and Y. Zhang, “An Overview of Content-Based Image Retrieval Techniques,” AINA’04, IEEE, 2004.
-  P. Y. Yin, B. Bhanu, K. C. Chang, and A. Dong, “Integrating Relevance Feedback Techniques for Image Retrieval using Reinforcement Learning,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.27, No.10, October, 2005.
-  P. Y. Yin, B. Bhanu, K. C. Chang, and A. Dong, “Reinforcement Learning for Combining Relevance Feedback Techniques,” ICCV, 2003.
-  Q. Iqbal and J. K. Aggarwal, “Feature Integration, Multi-Image Queries and Relevance Feedback in Image Retrieval,” Int. Conf. on Visual Information Systems (VISUAL), September, 2003.
-  V. Chitkara, “Color-Based Image Retrieval Using Compact Binary Signatures,” TR 01-08, Department of Computing Science, University of Alberta, 2001.
-  H. Yoo, H. Park, and D. Jang, “Expert System for Color Image Retrieval,” Expert Systems with Application, 2005.
-  I. Alfina and M. R. Widyanto, “Sistem Temu Kembali Citra untuk Sensasi Berbasis Teori Fuzzy,” National Conf. on Computer Science & Information Technology, University of Indonesia, 2007.
-  D. Kim, C. Chung, and K. Barnard, “Relevance Feedback using Adaptive Clustering for Image Similarity Retrieval,” J. of Systems and Software, Vol.78, October, 2005.
-  J. M. Garibaldi and R. I. John, “Choosing Membership Function of Linguistic Terms,” Automated Scheduling, Planning and Optimisation Group, University of Nottingham, UK.
-  M. Hellman, “Fuzzy Logic Introduction,” Laboratoire Antennes Radar Telecom, F.R.E CNRS 2272, Equipe Radar Polarimetrie, Universite de Rennes, 2001.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.