Image segmentation method and system based on feature driven heuristic four-color label

Abstract

The invention discloses an image segmentation method and system based on a feature driven heuristic four-color label, and relates to the field of machine vision. The method comprises the following steps that a meanshift method is used, and initialized segmentation is carried out on an input image; global grouping is carried out on the image after initialized segmentation, distribution of an initial area of a feature space is analyzed, and a similar matrix in an area set of initialized segmentation serves as AP clustering input; unnecessary adjacency is cracked via an adjacent relation crackingalgorithm, so that uniform adjacent areas can be marked in the same color; a heuristic four-color label algorithm is used to establish an internal coloring relation adaptively; and an MMPC model is combined with a GAC model to establish an MMPC-GAC model, MMPC-GAC modeling and MLG optimization are carried out iteratively till convergence is reached, and a final four-color segmented image is obtained. According to the invention, the uniform adjacent areas can be marked in the same color, and a uniform appearance area becomes globally consistent.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an image segmentation method and system based on a feature-driven heuristic four-color label. Background technique [0002] Image segmentation algorithms can be roughly divided into three categories, namely unsupervised methods, semi-supervised methods and supervised methods. These three methods are interrelated and the boundary lines are not very clear. Taking an unsupervised approach, image segmentation is performed without human intervention; dealing with images with coarse priors, such as occasional subtitles, user doodles, and annotations can be viewed as a semi-supervised approach. [0003] Segmentation problems are essentially clustering problems, where the goal is to group pixels into locally uniform regions. K-means (K-means), mean-shift (mean shift), region merging, and region segmentation are typical examples of clustering-based methods. Specifically, the K-means algor...

AI Technical Summary

Problems solved by technology

Another obvious weakness of the random coloring strategy is: assigning the same color to different regions, or assigning different colors to uniform regions

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