A self-adaptive ant colony optimization approach for image segmentation

Jue Lu

doi:10.1117/12.657993

4 January 2006 A self-adaptive ant colony optimization approach for image segmentation

Jue Lu

Proceedings Volume 5985, International Conference on Space Information Technology; 59853F (2006) https://doi.org/10.1117/12.657993
Event: International Conference on Space information Technology, 2005, Wuhan, China

Abstract

Ant colony optimization, with good discretion, parallel, robustness and positive feedback, is well suited to image segmentation. But its search is random and has much computation for convergence. Constant evaporating coefficient leads to early convergence or stagnation. To improve it, the ideal of setting primary cluster center is proposed. Meanwhile, the algorithm is implemented in a small window so as to reduce its computation. The evaporating coefficient is also set to change with the number of ants which pass the allowable path in order to keep its good global convergence and stability. This method can segment an image accurately. Experimental results show it's an effective approach.

Citation Download Citation

Jue Lu "A self-adaptive ant colony optimization approach for image segmentation", Proc. SPIE 5985, International Conference on Space Information Technology, 59853F (4 January 2006); https://doi.org/10.1117/12.657993

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