Color and texture image segmentation using uniform local binary patterns

Authors:
Sara Arasteh;Chih-Cheng Hung
Affiliations:
School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA;School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA
Venue:
Machine Graphics & Vision International Journal
Year:
2006

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Abstract

The paper describes a new algorithm for image segmentation based on the color and texture features. The Uniform Local Binary Pattern (ULBP) method is used to extract texture features. Color features are defined based on the pixels color bands. Image segmentation is carried out using the K-means algorithm on feature vectors, including color and texture features. The distance measure is defined as a function of the color and texture feature vector distances from the K-means defined centers. The weighting parameter is used to adjust the relative contribution of the color and texture features. The proposed algorithm is applied to color images in the RGB, HSV and IHLS color spaces. Experimental results show that the proposed algorithm yields good performance in combining color and texture features to distinguish different texture patterns. In particular, for textures with high color contrast, the results are prominent. The main advantage of the method is its speed and simplicity, which are inherited from the K-means algorithm.