Color Image Edge Detection Algorithm Based on K-means and Improved Ant Colony Optimization

doi:10.3969/j.issn.1006-2475.2016.09.013

Abstract

Abstract: In order to improve the accuracy of image edge detection, this paper proposes a color image edge detection algorithm based on K-means ant colony optimization. By embedding the clustering in edge detection, the two kinds of algorithms about image segmentation are combined effectively and the advantages are enhanced. Experimental results show that the proposed algorithm solves the problem of slow convergence in traditional ACO. Compared with the typical segmentation methods, it also better retains the image edge details and reduces the computational complexity, which has a better performance.

Key words: image segmentation, edge detection, K-means, clustering, ant colony optimization

CLC Number:

TP391

LIU Bo-ping1,2, MENG Sha-sha1,2, RAO Lan-xiang1,2, FU Kang1,2. Color Image Edge Detection Algorithm Based on K-means and Improved Ant Colony Optimization[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.09.013.

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