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http://dx.doi.org/10.3837/tiis.2011.01.012

Image Clustering using Color, Texture and Shape Features  

Sleit, Azzam (Computer Science Department, King Abdulla II School for Information Technology)
Abu Dalhoum, Abdel Llatif (Computer Science Department, King Abdulla II School for Information Technology)
Qatawneh, Mohammad (Computer Science Department, King Abdulla II School for Information Technology)
Al-Sharief, Maryam (Computer Science Department, King Abdulla II School for Information Technology)
Al-Jabaly, Rawa'a (Computer Science Department, King Abdulla II School for Information Technology)
Karajeh, Ola (Computer Science Department, King Abdulla II School for Information Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.5, no.1, 2011 , pp. 211-227 More about this Journal
Abstract
Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.
Keywords
Content-based image retrieval; HSV color histogram; Gabor filter; Fourier descriptor; k-means clustering;
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Times Cited By Web Of Science : 2  (Related Records In Web of Science)
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