Content based Color Image Clustering

Never before in history has image data been generated at such high volumes as it is today. If images are analyzed properly, they can reveal useful information to the users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image clustering involves the extraction of features from image databases and then application of data mining algorithm to group images. In this paper a data mining approach to cluster the images using color and texture features are proposed. Three techniques are proposed to extract Color feature, using Color Moments, Block Truncation Coding algorithm and histogram method. To extract texture feature concept of Gray Level Co-occurrence Matrix is extended and applied to color images. K-means clustering algorithm is applied to groups the images.