Maximum Fuzzy Entropy and Immune Clone Selection Algorithm for Image Segmentation

Author

Tian, WenJie ; Geng, Yu ; Liu, JiCheng ; Ai, Lan

Author_Institution

Autom. Inst., Beijing Union Univ., Beijing, China

Volume

1

fYear

2009

fDate

18-19 July 2009

Firstpage

38

Lastpage

41

Abstract

This paper is concerned with fuzzy entropy definition used for image segmentation. The key problem associated with this method is to find the optimal parameter combination of membership function so that an image can be transformed into fuzzy domain with maximum fuzzy entropy. An improved immune clone selection algorithm (ICSA) is proposed to search the optimal parameter combination. Then, we compare the proposed ICSA with other artificial intelligence models. The experiment indicates that the proposed method is quite effective and ubiquitous.

Keywords

entropy; fuzzy set theory; image segmentation; artificial intelligence model; image segmentation; immune clone selection algorithm; maximum fuzzy entropy; membership function; Artificial intelligence; Automation; Cloning; Entropy; Histograms; Image processing; Image segmentation; Immune system; Information processing; Pixel; image segmentation; immune clone selection algorithm; maximum fuzzy entropy; membership function; optimal parameter;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on

Conference_Location

Shenzhen

Print_ISBN

978-0-7695-3699-6

Type

conf

DOI

10.1109/APCIP.2009.18

Filename

5196990