Noise-robust binary segmentation based on ant colony system and modified fuzzy C-means algorithm

Authors:
Zhiding Yu;Ruobing Zou;Simin Yu;Huqiong Mou
Affiliations:
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong;School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China;School of Automation, Guangdong University of Technology, Guangzhou, China;Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

The wide application of Binary segmentation for grayscale images could be found in computer vision and pattern recognition, especially for the purpose of object identification and recognition with industry and military images. This paper proposes a noise robust binary segmentation approach which incorporates Ant Colony System (ACS) with the modified Fuzzy C-Means (FCM) clustering algorithm. The ACS first survey the whole image, adding an additional pheromone dimension other than grayscale on each pixel. The modified FCM then deems every pixel a 2-dimensional vector and classifies all image pixels into two categories. Experiments have demonstrated better segmentation results and the advantage of robustness against noise using this method.