• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Image Clustering using Color, Texture and Shape Features


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 kmeans 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.


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Cite this article

[IEEE Style]
Azzam Sleit, Abdel latif Abu dalhoum, Mohammad Qatawneh, Maryam Al-Sharief, Rawa¡¯a Al-Jabaly and Ola Karajeh, "Image Clustering using Color, Texture and Shape Features," KSII Transactions on Internet and Information Systems, vol. 5, no. 1, pp. 211-227, 2011. DOI: 10.3837/tiis.2011.01.012

[ACM Style]
Sleit, A., dalhoum, A. l. A., Qatawneh, M., Al-Sharief, M., Al-Jabaly, R., and Karajeh, O. 2011. Image Clustering using Color, Texture and Shape Features. KSII Transactions on Internet and Information Systems, 5, 1, (2011), 211-227. DOI: 10.3837/tiis.2011.01.012