Structure-sensitive color image segmentation super-pixelating method with boundary constraint

Abstract

A structure-sensitive color image segmentation super-pixelating method with boundary constraint belongs to the field of image processing of computer application technology, and is characterized by comprising the following steps: (1) completing the segmentation super-pixelating initialization construction of a color image by using linear iteration technology; (2) carrying out color image manifold space mapping and superpixel sensitive area calculation; (3) carrying out super-pixel CVT unit division and local k-means clustering; and (4) superpixelating the structure-sensitive color image with the boundary constraint. Advantages and positive effects are as follows: when the image region sensitivity is considered, target image boundary information is introduced, the possibility that the current pixel falls on the real boundary of the image is calculated through the boundary item; the super-pixel edges are redistributed and optimized, so that the super-pixel segmentation edges are better attached to the actual boundary, small super-pixels are generated in the area with the large density, large super-pixels are generated in the area with the small density, the attaching degree of the color image super-pixel segmentation edges and the actual boundary is improved, and the color image segmentation precision is improved.

Description

technical field [0001] The invention belongs to the image processing field of computer application technology, and in particular relates to a structure-sensitive color image segmentation superpixel method with boundary constraints. Background technique [0002] In the traditional image segmentation method, the pixel is the basic processing object, without considering the spatial and organizational relationship between pixels. With the rapid development of imaging technology and computer vision technology, the size of the image to be segmented is getting larger and higher, and the resolution is getting higher and higher, which leads to the low processing efficiency of the existing method. For this reason, the researchers proposed the concept of super pixelation . The so-called super-pixelation refers to an image block composed of multiple spatially adjacent pixels with similar color, texture, brightness and other characteristics in a digital image. Usually, the image is pre...

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Problems solved by technology

[0005] These algorithms are simple and efficient, and the image segmentation speed is fast, but they have defects in the superpixel similarity measurement and search strategy. Superpixels cannot be divided well, resulting in problems such as the target boundary and the boundary of the image to be segmented cannot be well fitted after superpixelation, and the under-segmentation error of the target image needs to be improved.

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