Probability hypergraph construction method based on space, color and central bias priori

Abstract

The invention provides a probability hypergraph construction method based on space, color and central bias priori, and the method comprises the steps: dividing an input image into a plurality of imageregions, calculating the characteristics of each image region, and defining each image region as the vertex of a probability hypergraph; Constructing a spatial hyperedge for each vertex based on spatial priori, constructing a color hyperedge for each vertex based on color priori, constructing a central bias hyperedge for each vertex located at the edge of the image based on central bias priori, and enabling the probability that each vertex in the hyperedge belongs to the hyperedge to be equal to the feature similarity between the vertex and a centroid point; Wherein the hyperedge in the hyper-graph is a set of three hyperedges, and defining the weight of the hyperedge as a quadratic sum of the probability that each vertex in the hyperedge belongs to the hyperedge; According to the method,the probability hypergraph is constructed by fully considering space priori, color priori and central bias priori, the complex relationship between image regions in the complex natural scene image can be effectively described, and the method is beneficial to carrying out significant target detection in the complex natural scene image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for constructing a probability hypergraph based on space, color and center bias priors. Background technique [0002] In recent years, some researchers have proposed graph-based methods to process images. These methods describe the input image with a simple graph describing the binary relationship between two vertices. In a simple graph, the image region is defined as the vertices of the graph, two similar image regions are connected as the edges of the graph, and the edge weight is defined as the similarity between the vertices. The simple graph can describe the information contained in the image concisely and conveniently, and has achieved certain results in the field of image processing. [0003] However, using a simple graph to describe the binary relationship between image regions cannot describe all the key information in the input image. In...

AI Technical Summary

Problems solved by technology

However, there are few existing studies on hypergraphs, and the key information contained in complex natural scene images cannot be described yet.

Images