Image Retrieval based on Modified Fuzzy C-Means Clustering Algorithm

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


content-based image retrieval (CBIR), clustering retrieval(CR), fuzzy C-means (FCM), modified fuzzy C-means (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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