Improved Research on C-means Clustering Algorithm based on Relieft Feature Weighting

10.6919/ICJE.202205_8(5).0016

Jiangtao Wang

Clustering ； C-means ； Feature Weighting ； Relieft Technique ； Fuzzy Set

International Core Journal of Engineering

8卷5期（2022 / 05 / 01）

123 - 130

英文

Image segmentation is the most critical step in image processing and image analysis. I have studied many articles on image segmentation techniques. First, the C-means clustering algorithm is clarified and related concepts are explained, and the basic principles and clustering criteria of the clustering method are analyzed. Then, in view of the advantages and disadvantages of the algorithm and the existing shortcomings, the C-means clustering algorithm is improved, and Relieft technology is introduced. Because many features are involved in image segmentation, the core of the improved algorithm is the weighting process during feature extraction, and the final design An effective and robust color image segmentation process is developed. Through a large number of experiments, the segmentation results of the C-means image segmentation algorithm before and after the improvement are compared. The improved clustering algorithm can indeed achieve better image segmentation results; through the experimental data placed in different environments Statistics can verify that the improved segmentation algorithm is more robust. Finally, the author analyzes the shortcomings of the algorithm model, and also points out the direction of future research.

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