An Image Segmentation Method Based on Ising Graph Model

Abstract

The invention discloses an image segmentation method based on an Ising graph model, comprising the steps of: constructing the Ising graph model corresponding to the graph, a dual graph corresponding to the Ising image model and an extension dual graph corresponding to the dual graph; calculating a maximum weight value perfect match of the extension dual graph according to the system total energy of the Ising graph model; obtaining a minimum weight value cut of the Ising graph model according to the maximum weight value perfect match of the extension dual graph, and obtaining the segmentation result of the image according to states of the nodes in the Ising graph model corresponding to the minimum weight value cut. The simple and effective Ising graph model is adopted for segmenting the image, therefore, not only the calculation complexity is low and the efficiency is high, but also the segmentation accuracy is high; meanwhile, compared with the traditional image segmentation algorithm, the image segmentation method based on the Ising graph model does not have too strict condition limitation; According to the image segmentation method, while calculating the weight value energy of the edges of the Ising graph model, the gray information or color information or texture information of the nodes in the Ising graph model are fully utilized, and the relatively accurate segmentation result can be achieved by regarding the information as the basis of the image segmentation.

Description

technical field [0001] The invention relates to an image segmentation technology, in particular to an image segmentation method based on an Ising graph model. Background technique [0002] Image segmentation technology is the most basic and important content in the field of computer vision. Accurate segmentation results and efficient processing can make image segmentation technology have important applications in many fields. Image segmentation is mainly to extract the target object in the image, and then edit the image. The main purpose is to serve as the basis of visual processing to realize the recognition and interpretation of the target object in high-level vision. The specific thing to do in image segmentation is to determine a label for each pixel in the image. This label represents the segmentation result, and the method of determining the label directly affects the quality of the image segmentation result. The research on image segmentation technology generally use...

AI Technical Summary

Problems solved by technology

The above reasoning algorithm is suitable for the field of image segmentation, but its reasoning time is relatively long and the efficiency is relatively low

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