Image Clustering using Color, Texture and Shape Features

• Azzam Sleit,
• Abdel latif Abu dalhoum,
• Mohammad Qatawneh,
• Maryam Al-Sharief,
• Rawa¡¯a Al-Jabaly,
• Ola Karajeh,

Vol. 5, No. 1, January 30, 2011
10.3837/tiis.2011.01.012, Download Paper (Free):

• Content-based image retrieval
• HSV color histogram
• Gabor filter
• Fourier descriptor
• K-Means Clustering

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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