Title :
Image Segmentation Based on Local Ant Colony Optimization
Author :
Zou, Ruobing ; Yu, Weiyu ; Yu, Zhiding ; Yu, Xiangyu
Author_Institution :
Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
In this paper, we proposed an improved image binary segmentation based Ant Colony Algorithm. Within different image areas, different iteration numbers and steps have been set for ants, achieving superior image segmentation results. Experimental results indicate the proposed method can enhance segmentation accuracy and reduce running time, thus possessing considerable application potential.
Keywords :
artificial life; fuzzy set theory; image classification; image segmentation; optimisation; pattern clustering; fuzzy C-means clustering; image area; image binary segmentation; image pixel classification; iteration number; local ant colony optimization; Ant colony optimization; Application software; Clustering algorithms; Computer vision; Image analysis; Image processing; Image segmentation; Lighting; Neural networks; Pixel;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.647
