Unsupervised color–texture segmentation based on multiscale quaternion Gabor filters and splitting strategy

This paper proposes a new method for color–texture segmentation based on a splitting framework with graph cut technique. To process the scale difference of quaternion Gabor filter (QGF) features of a color textured image, a new multiscale QGF (MQGF) is introduced to describe texture attributes of the given image. Then, the segmentation is formulated in terms of energy minimization gradually obtained using binary graph cuts, where color and MQGF features are modeled with a multivariate finite mixture model, and minimum description length (MDL) principle is integrated into this framework as a splitting criterion. In contrast to previous approaches, our method finds an optimal segmentation by balancing energy cost and coding length, and the segmentation result is determined during the splitting process automatically. Experimental results on both synthetic and real natural color textured images demonstrate the good performance of the proposed method.

► A new multiscale quaternion Gabor filter is introduced to extract texture features. ► A new Splitting framework is used to find an optimal segmentation. ► The proposed method can decide the segment result in unsupervised way.