Unsupervised Color Image Segmentation Using Graph Cuts with Multi-components

A novel unsupervised color image segmentation method based on graph cuts with multi-components is proposed, which finds an optimal segmentation of an image by regarding it as an energy minimization problem. First, L*a*b* color space is chosen as color feature, and the multi-scale quaternion Gabor filter is employed to extract texture feature of the given image. Then, the segmentation is formulated in terms of energy minimization with an iterative process based on graph cuts, and the connected regions in each segment are considered as the components of the segment in each iteration. In addition, canny edge detector combined with color gradient is used to remove weak edges in segmentation results with the proposed algorithm. In contrast to previous algorithms, our method could greatly reduce computational complexity during inference procedure by graph cuts. Experimental results demonstrate the promising performance of the proposed method.