Color image segmentation algorithm based on histograms

Abstract

The invention provides a color image segmentation algorithm based on histograms. The method includes the following steps that firstly, the color image RGB three-component histograms are counted and preprocessed respectively, so that the waveforms of the histograms are kept as smoother as possible; secondly, the histograms are searched for wave troughs through a wave crest and wave trough quick positioning algorithm, and the wave troughs serve as threshold values so that the histograms can be divided into multiple levels; thirdly, the divided histograms are combined again, a new histogram is established again, the histograms are searched for the wave troughs through the wave crest and wave trough quick positioning algorithm again, the histogram is divided into multiple levels, and then an initial clustering center is determined; finally, super-pixels are extracted by segmenting a color image in advance, segmentation areas serve as sample data, and the sample data are clustered in a fuzzy mode according to the determined clustering center. According to the color image segmentation algorithm, execution efficiency and clustering performance of a color image fuzzy clustering algorithm are effectively improved, and effectiveness of the algorithm is verified through running time and PRI indexes.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a color image segmentation algorithm. Background technique [0002] Image segmentation is a basic problem in image processing, and it is also a research problem. It has a very wide range of applications in the fields of pattern recognition, computer vision, machine learning, and medical image processing. According to whether the image to be processed contains color information, image segmentation technology can be divided into grayscale image segmentation technology and color image segmentation technology. [0003] Grayscale image segmentation technology is mainly used to process text images, industrial images, medical images, etc., mainly including edge detection algorithms, clustering algorithms, threshold segmentation algorithms, segmentation algorithms based on partial differential equations, etc. Color image segmentation technology is mainly used to process natural i...

AI Technical Summary

Problems solved by technology

However, due to the large number of optimization iterations or sample data of these algorithms, the clustering process is time-consuming, the real-time performance of the algorithm is poor, and the clustering performance still needs to be further improved.

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