Image segmentation method based on layered high-order conditional random field

Abstract

The invention discloses an image segmentation method based on a layered high-order conditional random field model, and the method comprises the steps: firstly extracting a plurality of types of texture features of a target image, and constructing a pixel-level unary potential function and a pixel-level paired potential function; obtaining superpixel segments with different granularities by using an unsupervised segmentation algorithm; designing a unary potential function and a paired potential function of each granularity layer corresponding to the superpixel level; constructing a layered high-order conditional random field model; supervising and learning hierarchical high-order conditional random field model parameters by utilizing the artificially marked samples; and finally, carrying out model reasoning on the to-be-tested image to obtain a final segmentation marking result. The layered high-order conditional random field model adopted by the invention fuses the multi-feature texture information and the multi-layer superpixel segmentation information of the image, so that the boundary segmentation accuracy of the multi-target object in the image can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of image segmentation, in particular to an image multi-object segmentation method based on hierarchical high-order conditional random fields. Background technique [0002] Image segmentation is a key problem in the field of computer vision. The quality of image segmentation has an important impact on subsequent applications such as image content analysis and pattern recognition. The current image segmentation algorithms mainly include the following categories: 1) Image segmentation based on threshold. This type of method is suitable for target images where the target and background have different gray scale ranges. 2) Region-based image segmentation. Its idea is based on the pixels with similar characteristics, through the image segmentation technology of region growing and region merging. 3) Segmentation based on deformation model. Such methods need to give the initial closed segmentation curve of the ...

AI Technical Summary

Problems solved by technology

Although this method has the characteristics of fast operation, the conditional random field model based on superpixels [4] usually enforces the consistency of the classification labels of all pixels in a superpixel, resulting in the segmentation results relying heavily on unsupervised segmentation algorithms pros and cons

For example, if the granularity of superpixel segmentation is not appropriate, a superpixel may contain different targets at the same time, and often the final segmentation accuracy is not as good as the pixel-based conditional random field model.

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