Abstract
In this paper, we proposed a method that based on image boundary detection and game theory for multiple labeling in image segmentation. We develop an approach to find the boundaries of multiple objects in the image and then connect the edge segment into a whole path that can be seen as the contour of the object. Furthermore, we can adapt our process into various types of scribbles depending on user’s application. The method is unsupervised and can solve multiple labeling for image segmentation. In our experiment, we use Spectral Matting as demonstration of our method. We use the area that each path is located and pick the brightest and darkest pixel’s RGB value from the path’s segment patch. Then we can construct the probability map for user’s foreground and background labels. We choose the pixel which bypass the threshold as foreground label; and let the pixels which under the threshold become background label. We use the label result as input scribbles of Spectral Matting to obtain a proper image alpha map. We compare our result generated from supervised version of Spectral Matting with the unsupervised ones. We can find multiple subtle objects in an image. Nevertheless, the unsupervised Spectral Matting tends to obtain a flat area or incomplete object boundary. Therefore, our method ensures a better result in an unsupervised favor and just cost a little computational time.