Hypergraph spectrum analysis based image segmentation method

Abstract

The invention discloses a hypergraph spectrum analysis based image segmentation method which specifically includes the steps of image preprocessing, image superpixel uniting, hypergraph model generation and hypergraph spectrum clustering. Areas after image over-segmentation are used as superpixels which are taken as vertexes, relation among the superpixels is obtained via various different over-segmentation methods to construct a hyperedge so as to form a hyperedge model, a traditional method for establishing relation (edge) between every two pixels to form a graph model is replaced, and accordingly high-order coupling relation among the pixels is reflected effectively, relevance among the areas of an image is considered, and area consistency and edge accuracy of a segmentation result are enabled to be obviously improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision and image processing, and in particular relates to an image segmentation method based on hypergraph analysis. Background technique [0002] In the field of computer vision, image segmentation (Segmentation) refers to the process of subdividing a digital image into multiple image sub-regions. The purpose of image segmentation is to simplify or change the representation of images, which is the basic work in the field of image processing and computer vision, making images easier to understand and analyze. In reality, although humans can easily divide images into coherent regions, it is very difficult for computer vision systems. Although many segmentation methods have been proposed, due to the diversity and ambiguity of natural images, the performance of segmentation cannot meet the actual needs. [0003] At present, the number of image segmentation methods is very large, among them, method...

AI Technical Summary

Problems solved by technology

[0013] 1) No unary (Unary) item is embedded, such as the priors of the respective graph nodes, which is equivalent to zero priors for all nodes;

[0014] 2) It is necessary to calculate the generalized eigenvector of a large matrix, although the complexity suppression measures are taken, the calculation is still very large

[0016] However, for the image model, only based on the traditional two-two pixel points to establish the relationship (edge), the formed graph model is obviously unable to describe the complex relationship between image regions, and the results of image segmentation are often unsatisfactory.

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