Integration of color and texture cues in a rough set–based segmentation method

Abstract. We propose the integration of color and texture cues as an improvement of a rough set–based segmentation approach, previously implemented using only color features. Whereas other methods ignore the information of neighboring pixels, the rough set–based approximations associate pixels locally. Additionally, our method takes into account pixel similarity in both color and texture features. Moreover, our approach does not require cluster initialization because the number of segments is determined automatically. The color cues correspond to the a and b channels of the CIELab color space. The texture features are computed using a standard deviation map. Experiments show that the synergistic integration of features in this framework results in better segmentation outcomes, in comparison with those obtained by other related and state-of-the-art methods.

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