A Practical GrabCut Color Image Segmentation Based on Bayes Classification and Simple Linear Iterative Clustering

Abstract

In order to solve the segmentation degradation phenomenon when the number of super pixels is low, we propose a novel color image segmentation algorithm based on GrabCut. The method integrates Bayes classification with simple linear iterative clustering (SLIC) and then use the GrabCut method to obtain the segmentation. The SLIC is applied to cluster the features of a color image and integrated it into the GrabCut framework to overcome the problem of the image segmentation deterioration when the number of super pixels is low. In addition, we extend the Gaussian mixture model (GMM) to SLIC features and GMM based on SLIC is constructed to describe the energy function. The color clustering can be suitably integrated into the GrabCut framework and fused with the color feature to achieve more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the Bayes classification is chosen for reconstruction of the simplified graph cut model instead of the original graph cut based on the SLIC model. The min-cut algorithm technique served as the division measure in the simplified image space for more discriminating power. A classification strategy is presented, to effectively adjust the energy function so that the Bayes classification and SLIC features are efficiently integrated to achieve more robust segmentation performance. Finally, boundary optimization is proposed to dramatically reduce the boundary roughness of the GrabCut algorithm with satisfactory segmentation accuracy. As a practical application, the superior performance of our proposed method was demonstrated through a large number of comparative tests.