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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Junhui Zhou
Hunan Vocational College for Nationalities
Computer Science & IT

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Applications of Improved Ant Colony Optimization Clustering Algorithm in Image Segmentation Junhui Zhou; Defa Hu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.1803

Abstract

When expressing the data feature extraction of the interesting objectives, image segmentation is to transform the data set of the features of the original image into more tight and general data set. This paper explores the image segmentation technology based on ant colony optimization clustering algorithm and proposes an improved ant colony clustering algorithm (ACCA). It improves and analyzes the computational formula of the similarity function and improves parameter selection and setting by setting ant clustering rules. Through this algorithm, it can not only accelerate the clustering speed, but it can also have a better clustering partitioning result. The experimental result shows that the method of this paper is better than the original OTSU image segmentation method in accuracy, rapidity and stability.
Co-Authors Defa Hu