Application of a hybrid ant colony optimization for the multilevel thresholding in image processing

Authors:
Yun-Chia Liang;Angela Hsiang-Ling Chen;Chiuh-Cheng Chyu
Affiliations:
Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, Taoyuan County, Taiwan, R.O.C.;Department of Financial Management, Nanya Institute of Technology, Chung-Li, Taoyuan County, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, Taoyuan County, Taiwan, R.O.C.
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

Our study proposes a hybrid optimization scheme based on an ant colony optimization algorithm with the Otsu method to render the optimal thresholding technique more applicable and effective. The properties of discriminate analysis in Otsu's method are to analyze the separability among the gray levels in the image. The ACO-Otsu algorithm, a non-parametric and unsupervised method, is the first-known application of ACO to automatic threshold selection for image segmentation. The experimental results show that the ACO-Otsu efficiently speed up the Otsu's method to a great extent at multi-level thresholding, and that such method can provide better effectiveness at population size of 20 for all given image types at multi-level thresholding in this study.