Earticle

초록

영어

In this paper, a novel image segmentation algorithm based on fuzzy clustering and entropy analysis using space information for optical images is proposed. We adopt the general properties of Hopfield neural network (HNN) and multi-synapse neural network (MSNN) to gain the center of the clusters and the fuzzy membership degrees for solving the optimization problems. As far as the noise influence is concerned, we introduce a novel window to improve the robustness of the proposed algorithm. In the experimental analysis part, we compare our method with some state-of-the-art methodologies and adopt the well-known test image databases to conduct the experiment. The result indicates that compared to FCM and some other clustering methods, our entropy and neural network based algorithm performs better. Our approach is less time-consuming and more robust to noise.

목차

Abstract
 1. Introduction
 2. The Fuzzy Clustering and Related Algorithms
  2.1. The Fuzzy c-means (FCM) Clustering
  2.2. The Generalized Entropy and Application
 3. Image Segmentation based on Proposed Method
  3.1 Modified Objective Function
  3.2. Capture Cluster Centers
  3.3. Capture Membership Degree
 4. Experiment and Analysis
 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 Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605