CMA:an efficient index algorithmof clustering supporting fast retrieval oflarge image databases  

作　　者：谢毓湘 栾悉道 吴玲达 老松杨 谢伦国 

机构地区：[1]Multi media R&D Center , National Univ .of Defense Technology [2]School of Computer Science , National Univ .of Defense Technology

出　　处：《Journal of Systems Engineering and Electronics》2005年第3期709-714,共6页系统工程与电子技术（英文版）

基　　金：This project was supported by National High Tech Foundation of 863 (2001AA115123)

摘　　要：To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast 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. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast 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. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.

关 键 词：large image database content-based retrieval K-means clustering self-adaptive clustering. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]