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Abstract
This paper proposes a clustering method called CMA, which supports content-based retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage – k-means iteration. We test our CMA algorithm on a large database of more than ten thousand images. Experiments show the effectiveness of this method.
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References
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Authors and Affiliations
Multimedia R&D Center, National University of Defense Technology, Changsha, 410073, China
Yu-Xiang Xie, Xi-Dao Luan, Ling-Da Wu & Song-Yang Lao
School of Computer Science, National University of Defense Technology, Changsha, 410073, China
Lun-Guo Xie
- Yu-Xiang Xie
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- Xi-Dao Luan
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- Ling-Da Wu
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- Song-Yang Lao
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- Lun-Guo Xie
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Editors and Affiliations
Department of Computer Science and Engineering, Shanghai Jiatong University, 80 Dongcuan Road, 200240, Shanghai, China
Minglu Li
Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA
Xian-He Sun
Department. of Computer Science, Shanghai Jiaotong University, 1954 HuaShan Road, 200030, Shanghai, P.R. China
Qianni Deng
Department of Computer Science, College of Liberal Arts and Science, University of Iowa, IA 52242, Iowa City, USA
Jun Ni
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© 2004 Springer-Verlag Berlin Heidelberg
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Xie, YX., Luan, XD., Wu, LD., Lao, SY., Xie, LG. (2004). An Efficient Clustering Method for Retrieval of Large Image Databases. In: Li, M., Sun, XH., Deng, Q., Ni, J. (eds) Grid and Cooperative Computing. GCC 2003. Lecture Notes in Computer Science, vol 3033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24680-0_26
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