Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 247-250.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Fuzzy C-means Color Image Segmentation Algorithm Combining Hill-climbing Algorithm

JIA Juan-juan1, JIA Fu-jie2   

  1. College of Technology and Engineering,Lanzhou University of Technology,Lanzhou 730050,China1
    School of Mathematics and Statistics,Lanzhou University,Lanzhou 730000,China2
  • Online:2019-02-26 Published:2019-02-26

Abstract: There are some problems with the color image segmentation technology based on traditional Fuzzy C-means clustering algorithm,such as the selection of the initial category number,the determinated of the initial centroids,large amount of calculation in clustering process and post-processing.Based on the research of these problems,according to the shortage of random initialization in traditional FCM,and for getting more accurate initialization automatically,this paper proposed a clustering segmentation method combining Hill-climbing for color image(HFCM),which can generate the initial centroids and the number of clusters adaptively according to the three dimensional histogram of the image.In addition,a new post-processing strategy which combined the most frequency filter and region mergeing was introduced to effectively eliminate small spatial regions.Experiments show that the proposed segmentation algorithm achieves high computational speed,and its segmentation results are close to human perceptions.

Key words: Fuzzy C-means clustering algorithm, Color image segmentation, Hill-climbing algorithm, Global three-dimensional color histogram

CLC Number: 

  • TP391.41
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