Abstract
Image segmentation is an important issue in image editing and computer vision. Due to the complexity of information in images, efficient extraction of a foreground object is a challenging problem. Recently, several approaches based on optimization by graph cuts have been developed which successfully combine the color feature with the edge information. A problem is that the segmentation results heavily depend on the seeds selection. However, it is difficult to obtaining reliable seeds automatically. To overcome this problem, we propose an automatic scheme for image segmentation. Compare to the classical binary-label graph cuts, the results by the multi-label graph cuts do not heavily depend on the seeds selection. Our method uses the multi-label graph cuts to separate an image into multiple segments, and then classify the segments into the object and the background. We introduce the standard deviation to adapt the importance between the properties in our method. Experiments show that the proposed method yields more accurate segmentation results than the previous automatic approach and is comparable to the interactive approach.