대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

엔트로피 기반의 가중치와 분포크기를 이용한 향상된 FCM 알고리즘

Improved FCM Algorithm using Entropy-based Weight and Intercluster

  • 곽현욱 (영남대학교 컴퓨터공학과) ;
  • 오준택 (영남대학교 컴퓨터공학과) ;
  • 손영호 (영남대학교 전자정보공학부) ;
  • 김욱현 (영남대학교 전자정보공학부)
  • Kwak Hyun-Wook (Department of Computer Engineering, Yeungnam University) ;
  • Oh Jun-Taek (Department of Computer Engineering, Yeungnam University) ;
  • Sohn Young-Ho (School of Electrical Engineering and Computer Science, Yeungnam University) ;
  • Kim Wook-Hyun (School of Electrical Engineering and Computer Science, Yeungnam University)
  • 발행 : 2006.07.01
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초록

본 논문은 엔트로피 기반의 가중치와 클러스터 분포크기를 이용한 향상된 FCM(Fuzzy C-Mean)알고리즘을 제안한다. FCM 알고리즘은 영상분할에서 일반적으로 많이 사용되는 퍼지 클러스터링 방법이다. 그러나 공간정보를 포함하지 않기 때문에 잡음 등에 민감하고, 클러스터를 이루는 특정들의 분포에 따라 화소들을 정확하게 분류할 수 없다. 이러한 단점을 해결하기 위해서 FCM 알고리즘의 소속정도를 연산할 때 클러스터 분포크기와 이웃 화소의 공간정보를 이용한 엔트로피 기반의 가중치를 적용한다. 실험결과에서 제안한 방법이 기존의 방법들보다 잡음에 강건하며 분할결과를 보였다.

This paper proposes an improved FCM(Fuzzy C-means) algorithm using intercluster and entropy-based weight in gray image. The fuzzy clustering methods have been extensively used in the image segmentation since it extracts feature information of the region. Most of fuzzy clustering methods have used the FCM algorithm. But, FCM algorithm is still sensitive to noise, as it does not include spatial information. In addition, it can't correctly classify pixels according to the feature-based distributions of clusters. To solve these problems, we applied a weight and intercluster to the traditional FCM algorithm. A weight is obtained from the entropy information based on the cluster's number of neighboring pixels. And a membership for one pixel is given based on the information considering the feature-based intercluster. Experiments has confirmed that the proposed method was more tolerant to noise and superior to existing methods.

키워드

참고문헌

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