The Transactions of the Korea Information Processing Society (한국정보처리학회논문지)

Korea Information Processing Society (한국정보처리학회)
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Image Segmentation Based on the Fuzzy Clustering Algorithm using Average Intracluster Distance

평균내부거리를 적용한 퍼지 클러스터링 알고리즘에 의한 영상분할

  • 유현재 ((주)DMS 연구원) ;
  • 안강식 (한국해양대학교 대학원 제어계측공학과) ;
  • 조석제 (한국해양대학교 제어계측공학과)
  • Published : 2000.09.01
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Abstract

Image segmentation is one of the important processes in the image information extraction for computer vision systems. The fuzzy clustering methods have been extensively used in the image segmentation because it extracts feature information of the region. Most of fuzzy clustering methods have used the Fuzzy C-means(FCM) algorithm. This algorithm can be misclassified about the different size of cluster because the degree of membership depends on highly the distance between data and the centroids of the clusters. This paper proposes a fuzzy clustering algorithm using the Average Intracluster Distance that classifies data uniformly without regard to the size of data sets. The Average Intracluster Distance takes an average of the vector set belong to each cluster and increases in exact proportion to its size and density. The experimental results demonstrate that the proposed approach has the g

영상분할은 컴퓨터비전 시스템에서 영상정보추출의 중요한 과정 중의 하나이다. 이중에서 퍼지 클러스터링 방법은 영상분할에 광범위하게 사용되고 있다. 대부분의 퍼지 클러스터링 방법으로는 FCM 알고리즘이 사용된다. 그러나 FCM 알고리즘은 클러스터의 중심과 데이터간의 거리에 의존하기 때문에 클러스터 크기가 다를 경우에는 데이터가 오분류될 수 있다. 본 논문에서는 클러스트 크기에 상관없이 데이터를 분류할 수 있는 평균내부거리를 이용한 퍼지 클러스터링 알고리즘을 제안하였다. 평균내부거리는 각 데이터로부터 해당 클러스터 중심까지의 거리를 평균한 값으로 클러스터의 크기와 밀도에 비례한다. 실험 결과를 통하여 제안된 방법이 분류 엔트로피와 적합도 함수에 의해서 좋은 결과를 보여주고 있음을 증명하였다.

Keywords

References

  1. Y. W. Lim and S. U. Lee, 'On the Color Image Segmentation Algorithm Based on the Thresholding and the Fuzzy C-Means Techniques,' Pattern Recognition, Vol.23, No.9, pp.935-952, 1990 https://doi.org/10.1016/0031-3203(90)90103-R
  2. H. Rhee and K.Oh, 'A Design and Analysis of Objective Function Based Unsupervised Neural Networks of Fuzzy Clustering,' Neural Processing Letters, Vol.4, pp.83-95, 1996 https://doi.org/10.1007/BF00420617
  3. D. N. Chun, 'A Method of Clustering and Image Segmentation Based on Fuzzy Genetic Algorithm,' Dept. of Computer Science. KAIST, pp.26-34, 1996
  4. R. C. Gonzalez, and R. E. Woods, Digital Image Processing, Addison Wesley, pp.443-457. 1992
  5. J. C. Bezdek and M. M. Rrivedi, 'Low Level Segmentation of Aerial Images with Fuzzy Clustering,' IEEE Trans. on Systems, Man, and Cybernetics, Vol.SMC-16, No.4, pp.589-598, 1986 https://doi.org/10.1109/TSMC.1986.289264
  6. R. Krishnapuram and J. M. Keller, 'A Possibilistic Approach to Clustering,' IEEE Trans. on Fuzzy Systems, Vol.1, No.2, pp.98-110, 1993 https://doi.org/10.1109/91.227387
  7. T. A. Runkler and J. C. Bezdek, 'Alternating Cluster Estimation : A New Tool for Clustering and Function Approximation,' IEEE Trans. on Fuzzy Systems, Vol.7, No.4, pp.377-393, 1999 https://doi.org/10.1109/91.784198
  8. P. R. Kersten, 'Fuzzy Order Statistics and Their Application to Fuzzy Clustering,' IEEE Trans. on Fuzzy Systems, Vol.7, No.6, pp.708-712, 1999 https://doi.org/10.1109/91.811239
  9. Y. El-Sonbaty and M. A. Ismail, 'Fuzzy Clustering for Symbolic Data,' IEEE Trans. on Fuzzy Systems, Vol.6, No.2, pp.195-204, 1998 https://doi.org/10.1109/91.669013
  10. James C. Bezdek, 'A Convergence Theorem for the Fuzzy ISODATA clustering algorithms,' IEEE Trans. on Pattern Anal. and Machine Intell., Vol. PAMI-2, No.1, pp.1-8, 1980
  11. N. Pal and J. Bezdek, 'On Cluster Validity for the Fuzzy C-Means Model,' IEEE Trans. on Fuzzy Systems, Vol.3, No.3, pp.370-379, 1995 https://doi.org/10.1109/91.413225
  12. R. Krishnapuram, H. Frigui and O. Nasraoui, 'Fuzzy and Possibilistic Shell Clustering Algorithms and Their Application to Boundary Detection and Surface Approximation,' IEEE Trans. on Fuzzy Systems, Vol.3, No.1, pp.29-60. 1995 https://doi.org/10.1109/91.366564
  13. A. P. Witkin, 'Scale-Space Filtering,' Processing IJCAl-83, pp.1019-1022, 1983
  14. X. Xie and G. Beni, 'A Validity Measure for Fuzzy Clustering,' IEEE Trans. on Pattern Anal. and Machine Intell., Vol.13, No.8, pp.841-847, 1991 https://doi.org/10.1109/34.85677