The Study Of Image Segmentation On Graph By Regularized Diffusion

Supervised image segmentation which provide the user with the ability of interacting with the algorithm is gradually worth paying more attentions, conventional method use the similarity between unknown pixel and the seed pixel to decide the label of the unknown pixel, but these kind of methods are not always producing good results because of the insufficient criteria of the similarity. These days, with the characteristics like discrete and well-posed, regularized diffusion method perform good in the filed of image denoising, we propose a new digital image segmentation method on graph based on regularized diffusion by analysising the essence of the regularized diffusion.We apply a regularized diffusion framework to solve the supervised learning image segmentation problem. The weight of the graph is generated by using Gaussian Kernel Function, combining with the geometric feature extracted from the image by using contourlet transform and the color feature by HSI decomposition. The graph topology structure is an improved 8-connection topology which step is 2 k , k = 0,1,2,3.Experimental results have shown that compared with some graph spectral theory based image segmentation algorithm, such as random walker and the Lazy Snapping, the proposed method is robust on noisy pictures, which can reserve a more complete boundary and have a better performance on the section with inconsistent texture. .One can consider the diffusion method as a kernel method. Morlet wavelet kernel is a translation invariant function based on Morlet wavelet transform, which make a terrific performance of reserving the complex boundary of the object on the denoise by diffusion. So by analysing the construction property of the weight of the diffusion framework, one can modify the formulation of the standard Morlet wavelet kernel function to apply it on the supervised learning image segmentation problem instead of using Gaussian Kernel Function. By computing the difference between the segment result and the known pattern, experimental results have shown that compared with algorithm using Gaussian Kernel Function, the algorithm using Morlet wavelet kernel function can do a better job with less feature supports, especially on the problem of reserving complex boundary section.