Earticle

초록

영어

Color image segmentation algorithms are proposed based on granular computing clustering (GrCC). Firstly, the atomic hyperspherical granule is represented as the vector including the RGB value of pixel of color image and radii 0. Secondly, the union operator of two hyperspherical granules is designed to obtain the larger hyperspherical granule compared with these two hyperspherical granules. Thirdly, the granular computing clustering is developed by the union operator and the user-defined granularity threshold . Global Consistency Error (GCE), Variation of Information (VI), Rand Index (RI), and Loss Entropy (ΔEn) are used to evaluate the segmentations. Segmentations of the color images selected from internet and BSD300 show that segmentations by GrCC speed up the segmentation process and achieve the better segmentation performance compared with Kmeans and FCM segmentations.

목차

Abstract
 1. Introduction
 2. Granular Computing Clustering Algorithm
  2.1. GrCC
  2.2. Inclusion Measure Function
 3. Color Image Segmentation Algorithms Based on GrCC
 4. Evaluation of Segmentation
  4.1. Global Consistency Error
  4.2. Variation of Information
  4.3. Rand Index
  4.4. Loss of Entropy
 5. Experiments
 6. Conclusions
 Acknowledgment
 References

키워드

저자

• Hongbing Liu [ School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, P. R. China ]
• Lei Li [ School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, P. R. China ]
• Chang-an Wu [ School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, P. R. China ]

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