imagesegmentation is a key technology from image processing to analysis. Without proper segmentation, it is impossible to recognize correctly. In this paper, we propose a method for imageco-segmentation based on the...
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imagesegmentation is a key technology from image processing to analysis. Without proper segmentation, it is impossible to recognize correctly. In this paper, we propose a method for imageco-segmentation based on the biased normalized cuts using a semi-supervised way to deal with foreground regions. In order to take advantage of biased normalized cuts to solve problem, we use 2 D adaptive Wiener filter to smooth the seeded parts of images, then divide images into a set of super-pixels, after that take super-pixels as vertices to form a weighted undirected graph. Thus, the co-segmentation can be seen as an issue of graph partition that solved by biased normalized cuts. The experiments on image data sets show the superior performance of our method.
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