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Two-phase Content-based Image Retrieval Using the Clustering of Feature Vector  

조정원 (한양대학교 전자통신전파공학과)
최병욱 (한양대학교 정보통신대학 정보통신학부)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.40, no.3, 2003 , pp. 171-180 More about this Journal
Abstract
A content-based image retrieval(CBIR) system builds the image database using low-level features such as color, shape and texture and provides similar images that user wants to retrieve when the retrieval request occurs. What the user is interest in is a response time in consideration of the building time to build the index database and the response time to obtain the retrieval results from the query image. In a content-based image retrieval system, the similarity computing time comparing a query with images in database takes the most time in whole response time. In this paper, we propose the two-phase search method with the clustering technique of feature vector in order to minimize the similarity computing time. Experimental results show that this two-phase search method is 2-times faster than the conventional full-search method using original features of ail images in image database, while maintaining the same retrieval relevance as the conventional full-search method. And the proposed method is more effective as the number of images increases.
Keywords
CBIR; Two-phase search method; Clustering; Fast retrieval;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Greg Pass, Ramin Zabih, and Justin Miller, 'Comparing Images Using Color Coherence Vectors,' Proc. of Fourth ACM Conf. on Multimedia, Boston, MA, pp.65-73, Nov. 1996   DOI
2 Wei-Ying Ma and HongJiang Zhang, 'Benchmarking of Image Features for Content -based Retrieval,' Proc. of Signals Systems & Computers, Conference Record of the Thirty-Second Asilomar Conference on , Vol. 1, pp. 253-257, 1998
3 P.N. Yianilos., 'Data structures and algorithms for nearest neighbor search in general metric spaces,' Proc. of the 4th Annual ACM-SIAM Symp. on Discrete Algorithms, pp. 311-321, 1993
4 L, Xu, A. Krzyzak, and W. Oja., 'Rival penalized competitive learning for clustering analysis, RBF net, and curve detection,' IEEE trans. on Neural Networks, 4(4):636-649, 1993   DOI   ScienceOn
5 D.E. Rumelhart and D. Zipser., 'Feature discovery by competitive learning,' Journal of Cognitive Science, 9:75-112, 1985   DOI   ScienceOn
6 H. Wolfson, 'Model-based object recognition by geometric hashing,' Proc. of European Conference Computer Vision, France, pp. 526-536, April 1990
7 Kishan Mehrotra, Chilukuri K. Mohan and Sanjay Ranka, Elements of Artificial Neural Networks, The MIT press, 1997
8 C. Faloutsos and K.-I. Lin, 'FastMap: a fast algorithm for indexing, data-mining, and visualization of traditional and multimedia data sets,' Proc. of 1995 ACM SIGMOD Int. Conf. on Management of Data, USA, pp. 163-174, May 1995   DOI
9 B.V. Bonnlander and A.S. Weigend, 'Selecting input variables using mutual information and nonparametric density estimation,' Proc. of Int. Symp. on Artificial Neural Networks, Taiwan, pp. 312-321, Dec. 1994
10 A. Califano and R. Mohan, 'Multidimensional indexing for recognizing visual shapes,' Proc. of IEEE Computer Vision and Pattern Recognition, pp, 28-34, June 1991   DOI
11 M. Beatty and B.S. Manjunath, 'Dimensionality reduction using multidimensional scaling for content-based retrieval,' Proc. of IEEE Int. Conf. Image Processing, USA, pp. 835-838, Oct, 1997   DOI
12 MPEG Requirements Group, MPEG-7 context, objectives and technical roadmap, Doc. ISO/ MPEG N2729, MPEG Seoul Meeting, Seoul, Korea, March 1999
13 William B.Frakes and Ricardo Baeza-Yates, Information Retrieval, Prentice Hall, 1992
14 Young Rui and Thoas S. Hang, Shih-Fu Chang 'Image Retrieval: Current techniques, promising directions, and open issues,' Journal of Visual Communication and Image Representation, vol. 10, pp. 39-62, 1999   DOI   ScienceOn