An ant colony approach for image texture classification

Zhiwei Ye; Zhaobao Zheng; Xiaogang Ning; Xin Yu

doi:10.1117/12.654796

3 November 2005 An ant colony approach for image texture classification

Zhiwei Ye, Zhaobao Zheng, Xiaogang Ning, Xin Yu

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.

Citation Download Citation

Zhiwei Ye, Zhaobao Zheng, Xiaogang Ning, and Xin Yu "An ant colony approach for image texture classification", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 60440Y (3 November 2005); https://doi.org/10.1117/12.654796

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KEYWORDS

Image classification

Chemical elements

Detection and tracking algorithms

Machine learning

Optimization (mathematics)

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Computer simulations

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