Earticle

초록

영어

Fuzzy c-means clustering (FCM) with spatial information (FCM_S) is an effective algorithm for image segmentation. However, the FCM_S algorithm is not used for color image segmentation and also it produces over-segmentation results. In this paper, we present a novel fuzzy c-means algorithm named nFCM_S that incorporates spatial information into the membership function and cluster center function for segmentation of color images. Firstly, HSV color space is used for decomposition of color images. Then, to label the data points reliably, a linearly-weighted sum image is calculated on each HSV component before clustering process. Finally, spatial information is incorporated in the standard FCM algorithm and nFCM_S is applied separately on each component of HSV color space. Experiment results have shown that the nFCM_S algorithm achieves competitive segmentation results compared to other FCM-based algorithms.

목차

Abstract
 1. Introduction 
 2. Fuzzy C-Means
 3. FCM_S Algorithm
 4. The nFCM_S Algorithm Incorporating Spatial Information for Color Image Segmentation
  4.1. HSV Color Space
  4.2. A Linearly-Weighted Sum Image for HSV Color Space
  4.3. A Novel FCM with Spatial Information
 5. Experimental Results
  5.1. Comparison of Segmentation Results on Color Images
  5.2. Comparison of Quality Evaluation with Different Algorithms
 6. Conclusions
 Acknowledgments
 References

키워드

저자

• Li Ling [ College of Communication Engineering, Jilin University, Changchun 130012, China ]
• Song Yingwei [ College of Communication Engineering, Jilin University, Changchun 130012, China ]
• Yin Zhongnan [ College of Communication Engineering, Jilin University, Changchun 130012, China ]
• Yang Xiuhua [ College of Communication Engineering, Jilin University, Changchun 130012, China ]

참고문헌

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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