Earticle

초록

영어

Aiming at partitioning an image into homogeneous and meaningful regions, automatic image segmentation is a fundamental but challenging problem in computer vision. It is well known that Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the FCM-based image segmentation algorithm must be manually estimated to determine cluster number by users. In this paper, we propose a novel cluster number adaptive fuzzy c-means image segmentation algorithm (CNAFCM) for automatically grouping the pixels of an image into different homogeneous regions when the cluster number is not known beforehand. We utilize the Grey Level Co-occurrence Matrix (GLCM) feature extracted at the image block level instead of at the pixel level to estimate the cluster number, which is used as initialization parameter of the following FCM clustering to endow the novel segmentation algorithm adaptively. We cluster image pixels according to their corresponding Gabor feature vectors to improve the compactness of the clusters and form final homogeneous regions. Experimental results show that proposed CNAFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality, in compare with those FCM-based segmentation methods recently proposed in the literature.

목차

Abstract
 1. Introduction
 2. The Standard FCM Algorithm
 3. Proposed Algorithm
  3.1. Basic Idea
  3.2. GLCM Texture Feature Extraction
  3.3. Cluster Validity
  3.4. FCM Clustering
  3.5. Pseudo Code
 4. Experimental Results
  4.1. Experiments Performed on Synthetic Images
  4.2. Experiments Performed on Natural Images
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Shaoping Xu [ School of Information Engineering, Nanchang University, NanChang,, JiangXi, China, 330031 ]
• Lingyan Hu [ School of Information Engineering, Nanchang University, NanChang,, JiangXi, China, 330031 ]
• Xiaohui Yang [ School of Information Engineering, Nanchang University, NanChang,, JiangXi, China, 330031 ]
• Xiaoping Liu [ School of Information Engineering, Nanchang University, NanChang,, JiangXi, Department of Systems and Computer Engineering, Carleton University, Ottawa, ON Canada, K1S 5B6. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605