Color Image Clustering using Block Truncation Algorithm

Silakari, Sanjay and Motwani, Mahesh and Maheshwari, Manish (2009) Color Image Clustering using Block Truncation Algorithm. [Journal (Paginated)]

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PDF (S. Silakari, M. Motwani and M. Maheshwari, "Color Image Clustering using Block Truncation Algorithm", International Journal of Computer Science Issues, IJCSI, Volume 4, Issue 2, pp31-35, September 2009) - Published Version


With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters.

Item Type:Journal (Paginated)
Keywords:Image features, Clustering, Color moments, BTC
Subjects:Computer Science > Human Computer Interaction
ID Code:6715
Deposited By: International Journal of Computer Science Issues, IJCSI
Deposited On:14 Nov 2009 11:30
Last Modified:11 Mar 2011 08:57


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