Image Segmentation Based On Improved Graph Cuts Method

With the development of technology of computer vision and digital image processing, image segmentation has becomes vital step of image processing and image analysis.Because interactive segmentation can achieve more accuracy result comparing with automatic method, it becomes more and more popular. And at the same time, Graph Cuts with its excellent behavior in segmentation field, has receives more and more attraction.Graph Cuts is an interactive segmentation method which can achieve global optimization. With the user assigning foreground and background seeds, it can segment out the object automatically.It can avoid the drawback of local optimization by conventional segmentation.Recently, Super Pixel has been used in image preprocessing, for that it can capture redundancy in image and greatly reduce the complexity of subsequent image processing tasks, and also for its ability to preserve boundary to make segment results more like an object.So we form a Graph Cuts base on Super Pixel instead of pixel level,which make a great reduce in the number of graph nodes and make the Graph Cuts more efficient. At the same time, we propose to use location, color, texture and shape combining feature to describe the Super Pixel. Comparing single feature descriptor, our method can capture more Super Pixel's information, making it works well in pair Super Pixel likelihood measure.Conventional Graph cuts'boundary term is determined by the image information itself, the result boundary mainly may appear in the interior of object.So that, we propose a pairwise Super Pixel learning to guide the setting of boundary term.With the supervise learning information,it can guide the Graph cuts to segment right boundary.