Research On Image Segmentation Algorithm Based On The Model Of Random Walk

Image segmentation is a process to make the computer extracting some practical meaningful area from the whole image according to the human cognition,and it plays a very important role in the field of image processing.Nowadays,electronic information technology is already highly developed,the rapid development of new technologies in various fields has brought new challenges and requirements to the research of image segmentation.Although with the gradual deepening of the research,more and more related articles have published,and the quality and efficiency of image segmentation have been greatly improved.However,image segmentation is the foundational process in many application systems,and the high-level applications in system depend largely on the accuracy of image segmentation result to understand the meanings of images.Therefore,the single traditional technique of image segmentation cannot meet the application requirements in terms of efficiency and accuracy.The image segmentation methods can be divided into two classes by judging whether there are any guidance information from human in the segmentation process: automatic methods and interactive methods.The random walk model is one of the interactive algorithms which has been widely concerned after introduced into the field of image segmentation because of its robustness,high precision and strong ability to handle weak boundaries.This paper focuses on the characteristics of the random walk model and its application improvement in image segmentation,the following research work was carried out:1)Summarize the existing image segmentation algorithms,and introduce the advantages and development status of the random walk model in image segmentation in detail.2)Combining the superpixel segmentation algorithm and the random walk algorithm,to overcome the drawbacks of the traditional random walk algorithm which process large images slowly and segment complex texture images poorly.The superpixel method enlarged the minimum computational unit of random walk,increases the speed effectively and improves the segmentation ability of random walk method on complex texture images.It makes the traditional random walk algorithm avoid the shortcomings that rely heavily on the single factor by introducing the region texture information in the random walk weight function,which make the similarity measure more accurate.The graph constructed by superpixel reduces the number of nodes greatly,which improve the speed of random walk significantly.3)Improve the random walk segmentation method through the proposed initial center led k_means algorithm.The clustering results of k_means which are improved by color distance and local search are used as the candidate marked regions of random walk.After the purification,those candidate regions used as the seed regions of the random walk to achieve image segmentation.This method reduces the time cost of interaction greatly,especially when dealing with multi target images.