Earticle

초록

영어

With the fast development of image processing technique, segmentation related issues have gained special attention in the research community. In this paper, an improved FCM combining mean shift algorithm is proposed to improve the segmentation visual effects and efficiency of traditional FCM. Initially, image segmentation into many small homogeneous area using the mean shift algorithm is conducted, segmentation and uniform area, rather than pixels as the new node. Then, image local entropy is adopted to describe the new nodes spatial and gray feature. Finally, an exponential function which is able to well simulate human nonlinear visual reaction was used to measure the similarity between the new node and the cluster center node. The experimental result shows the effectiveness and robustness of our proposed FCM, further potential research is also discussed.

목차

Abstract
 1. Introduction
 2. The Traditional FCM Algorithm
 3. Mean Shift Based FCM Algorithm
  3.1. The Mean Shift Pre-Segmentation
  3.2. The Image Entropy Characteristics
  3.3. The New Metric Formula
  3.4. The Flow Chart of MS-FCM Algorithm
 4. Experimental Analysis
  4.1. The Segmentation Performance
  4.2. The Efficiency of the Algorithm
 5. Conclusion and Summary
 References

키워드

저자

• Bingquan Huo [ Binzhou Polytechnic, Shandong, China ]
• Fengling Yin [ Binzhou Polytechnic, Shandong, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605