3 November 2005 An ant colony approach for image texture classification
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Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 60440Y (2005) https://doi.org/10.1117/12.654796
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China
Abstract
Ant colonies, and more generally social insect societies, are distributed systems that show a highly structured social organization in spite of the simplicity of their individuals. As a result of this swarm intelligence, ant colonies can accomplish complex tasks that far exceed the individual capacities of a single ant. As is well known that aerial image texture classification is a long-term difficult problem, which hasn't been fully solved. This paper presents an ant colony optimization methodology for image texture classification, which assigns N images into K type of clusters as clustering is viewed as a combinatorial optimization problem in the article. The algorithm has been tested on some real images and performance of this algorithm is superior to k-means algorithm. Computational simulations reveal very encouraging results in terms of the quality of solution found.
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Zhiwei Ye, Zhiwei Ye, Zhaobao Zheng, Zhaobao Zheng, Xiaogang Ning, Xiaogang Ning, Xin Yu, Xin Yu, } "An ant colony approach for image texture classification", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 60440Y (3 November 2005); doi: 10.1117/12.654796; https://doi.org/10.1117/12.654796
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