creators_name: Silakari, Sanjay 
creators_name: Motwani, Mahesh 
creators_name: Maheshwari, Manish 
type: journalp
datestamp: 2009-11-14 11:30:48
lastmod: 2011-03-11 08:57:33
metadata_visibility: show
title: Color Image Clustering using Block Truncation Algorithm
ispublished: pub
subjects: comp-sci-hci
full_text_status: public
keywords: Image features, Clustering, Color moments, BTC
abstract: 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.
date: 2009-09
date_type: published
publication: 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
volume: 4
number: 2
publisher: International Journal of Computer Science Issues, IJCSI
refereed: TRUE
citation:   Silakari, Sanjay  and Motwani, Mahesh  and Maheshwari, Manish   (2009) Color Image Clustering using Block Truncation Algorithm.  [Journal (Paginated)]     
document_url: http://cogprints.org/6715/1/4-2-31-35.pdf