Seeded image segmentation is a popular type of supervised image segmentation in computer vision and image processing. Previous methods of seeded image segmentation treat the image as a weighted graph and minimize an energy function on the graph to produce a segmentation. In this paper, we propose to conduct the seeded image segmentation according to the result of a heat diffusion process in which the seeded pixels are considered to be the heat sources and the heat diffuses on the image starting from the sources. After the diffusion reaches a stable state, the image is segmented based on the pixel temperatures. It is also shown that our proposed framework includes the RandomWalk algorithm for image segmentation as a special case which diffuses only along the two coordinate axes. To better control diffusion, we propose to incorporate the attributes (such as the geometric structure) of the image into the diffusion process, yielding an anisotropic diffusion method for image segmentation. The experiments show that the proposed anisotropic diffusion method usually produces better segmentation results. In particular, when the method is tested using the groundtruth dataset of Microsoft Research Cambridge (MSRC), an error rate of 4.42% can be achieved, which is lower than the reported error rates of other state-of-the-art algorithms.
['Juyong Zhang', 'Jianmin Zheng', 'Jianfei Cai']
A diffusion approach to seeded image segmentation