Research On Fast Segmentation Of Color Image Based On Improved GrabCut

Image segmentation using color, brightness, texture and shape features of the image to divided the image into regions with similar characteristics and differences between different portions; Image segmentation is a prerequisite for object recognition and image interpretation, graph cut is a typical method based on the energy function minimization. It is globally optimal, numerical robustness, segmentation with higher segmentation accuracy et al. But,when dealing with high resolution image, N-D image or image sequence,.Both the memory overhead and computation complexity, seriously restricting the availability of algorithm. The GrabCut algorithm proposed by Rother to further reduce the user interaction. It only requires the user to specify a rectangle around the target, It iteratively using graph cut method until satisfy convergence conditions, then you can get the target and background segmentation. Obviously this increased the calculation of Graphcuts.Superpixel is a pixel block with similar color, texture and gray feature. It uses the similarity between these features to segment the image into blocks, So it can provide simple information input for the calculation of image features,it can reduce the processing time for subsequent image processing tasks. Considering the GrabCut algorithm segmentation with high accuracy but their operation is slower, some Superpixel generation algorithm computing speed and their accuracy is not high split. In this paper,image segmentation is solved by GrabCut based on Superpixel. The work is summarized as follows:(1)Depth study GraphCuts,how to build a network diagram and network flow theory is described in detail. Details on how to build an energy function of the network graph. In-depth study GrabCut algorithms.GrabCut algorithms segmentation with high precision but split slow, the reason is iteration GraphCuts in the process of solving.(2)The Superpixel method are introduced, the judgment criterion of the performance of Superpixel algorithm is described.(3)Put forward fast segmentation in color image based on Quadtree decomposition and GrabCut. Quadtree decomposition algorithm segmentation with high speed, but over-segmentation and segmentation fault phenomena are serious.By Quadtree decomposition, the image is divided into several sub-blocks with internal high similarity to construct compact weighted graph,and the Gaussian Mixture Model (GMM) parameters can be estimated with the mean RGB values within blocks instead of all pixel values, thereby reducing the calculation of GMM parameter estimation,the maximum flow algorithm is accelerated, which greatly accelerated the speed of GMM parameter estimation. Finally, in order to ensure accuracy,we use the finally GMM parameter to segment the original image, which greatly improves the accuracy of segmentation algorithms. Experimental results show that the algorithm is fast and efficient.(4) Put forward fast color image segmentation based on GrabCut and Superpixel. Use a Superpixel method to improve GrabCut,at first,we use it to complete presegmentation, then the Gaussian Mixture Model parameters can be estimated with the mean RGB values within blocks instead of all pixel values, This greatly reduces the number of sample points used in GMM parameter estimation,which can greatly improve the speed of GMM parameter estimation. Finally, in order to ensure accuracy,we use the finally GMM parameter to segment the original image, which greatly improves the accuracy of segmentation algorithms.The Superpixel used in this paper cover that watershed, Entropy rate,Mean shift, Superpixel lattices and SLIC.we put forward GrabCut algorithm based on watershed, GrabCut algorithm based on Entropy rate,GrabCut algorithm based on Mean shift, GrabCut algorithm based on Superpixel lattices and GrabCut algorithm based on SLIC, Analysis of the pros and cons of several algorithms based on the experimental results.