首页|CMA: an efficient index algorithm of clustering supporting fast retrieval of large image databases
CMA: an efficient index algorithm of clustering supporting fast retrieval of large image databases
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NETL
NSTL
万方数据
维普
To realize content-based 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.
large image databasecontent-based retrievalK-means clusteringself-adaptive clustering
NETL
NSTL
万方数据
维普
