Image Retrieval based on Modified Fuzzy C-Means Clustering Algorithm

P. Zhang, P. Fu, J. Xiao, and D. Meng (PRC)


contentbased image retrieval (CBIR), clustering retrieval(CR), fuzzy Cmeans (FCM), modified fuzzy Cmeans (MFCM), information visualization


One of the most important issues in content-based image retrieval (CBIR) is how to construct effective organization and index to enhance image retrieval speed. Clustering is a kind of effective indexing method. In this paper, we proposed a modified fuzzy C-means (MFCM) clustering scheme to cluster the entire images database before retrieval, similar retrieval step is performed in a certain cluster based on example image, so the time of retrieval is saving. Experiments show that MFCM applied to image retrieval is effective in exactness and real-time property. It is superior to traditional fuzzy C-means clustering algorithm. In addition, we use a kind of clustering information visualization method to show the effectiveness of the scheme, and at the same time user can find image distribution in the database space easily.

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