Digital image over-segmentation edge removing method based on color similarity

Abstract

The invention discloses a digital image over-segmentation edge removing method based on color similarity, and mainly aims at eliminating over-segmentation area edges caused by a JSEG segmentation algorithm by virtue of color similarity. The digital image over-segmentation edge removing method comprises the following steps: firstly, performing statistic analysis on segmentation line information of JSEG segmentation result; secondly, acquiring primary line elimination result by using an edge removing algorithm of 'three-point line elimination;' finally, acquiring final line elimination result by using the edge removing algorithm of 'three-point line elimination.' The experiment shows that by adopting the method, an over-segmentation edge can be removed so as to improve the segmentation precision of a digital image, and the digital image segmentation result is greatly improved.

Description

technical field [0001] The invention mainly relates to digital image processing technology, in particular to a method for removing over-segmented edges of digital images based on color similarity. Background technique [0002] Image segmentation is an important image technology that has received extensive attention in both theoretical research and practical applications. The image segmentation method is the technology and process of dividing the image into several specific regions with unique properties and proposing the target of interest. It is a key step from image processing to image analysis. The quality of image segmentation directly affects the understanding of images. [0003] The JSEG image segmentation algorithm is a classic image segmentation method based on image color, and its advantage is that it considers both color information and spatial information. It fully considers the local information of the image and can obtain more accurate segmentation boundaries...

AI Technical Summary

Problems solved by technology

In the RAG method, the order of merging regions is related to the marking of the region, that is, the artificial numbering of the region. The order of marking is different, and the order of merging regions is different, resulting in different merging results, and the RAG method cannot guarantee the minimum feature difference in adjacent regions. The region of the NNG method is the first to be merged; the NNG method is an improvement on the RAG method. Although the merging result does not change with the labeling order, the NNG method only considers the local region to ensure that the region with the smallest feature difference is merged first, and the merging is not optimal. , and contains many redundant operations, the effect is not high

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