Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 158-160.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

K-means Image Segmentation Algorithm Based on Weighted Quality Evaluation Function

LIU Chang-qi1, SHAO Kun1, HUO Xing2, FAN Dong-yang1, TAN Jie-qing2   

  1. School of Computer and Information,Hefei University of Technology,Hefei 230000,China1;
    School of Mathematics,Hefei University of Technology,Hefei 230000,China2
  • Online:2019-06-14 Published:2019-07-02

Abstract: K-means clustering algorithm is a common way in image segmentation.As an unsupervised learning method,it can find the association rules from characteristics of grey levels,thus has a great capability of segmentation.However,due to its single classification basis and uncertainty of the initial cluster centers,this algorithm still has some defects in image segmentation.Aiming at this problem,this paper proposed a modified K-means algorithm for image segmentation.The new algorithm uses the improved iterative algorithm based on information entropy to select thresholds as the initial K-means clustering centers,and then puts forward a new weighted quality evaluation function for K-means algorithm to get better segmentation thresholds.The experimental results show that the improved algorithm has higher accuracy and stability than OTSU algorithm and traditional K-means algorithm in image segmentation.

Key words: Image segmentation, K-means algorithm, Pattern recognition, Threshold value selection

CLC Number: 

  • TP391
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