Title :
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
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;
Conference_Titel :
Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-0-7695-3699-6
DOI :
10.1109/APCIP.2009.18
