Earticle

초록

영어

The segmentation results of the traditional FCM based image segmentation algorithms are only determined by the distribution of pixel intensity in the feature space, and they does not take the spatial distribution of pixels into consideration, which make the segmentation results discrete in the spatial distribution. To solve this problem, a global spatial similarity metric and a global intensity similarity metric are proposed, and introduced to a new distance metric which is used to calculate the difference between pixels and cluster centers. In addition, a maximal similarity based class merging mechanism is employed to achieve more accurate image segmentation. The experiments demonstrate that, comparison with the FCM and KFCM based image segmentation algorithms, the proposed method produces more accurate and applicable segmentation results.

목차

Abstract
 1. Introduction
 2. FCM Based Image Segmentation
 3. Proposed Method
 4. Maximal Similarity Based on Class Merging
 5. Experimental Results
 6. Conclusion
 References

키워드

저자

• YufengYi [ Academy of Opt-Electronics, China Electronic Technology Group Corporation, Tianjin, China / Key Laboratory of Electronics Information Control and Security Technology, China Electronic Technology Group Corporation, Sanhe, China ]
• Lei Wang [ Department of Public Order, National Police University of China, Shenyang, China ] Corresponding Author

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