Path: Top -> Journal -> Telkomnika -> 2015 -> Vol 13, No 3: September

Applications of Improved Ant Colony Optimization Clustering Algorithm in Image Segmentation

Applications of Improved Ant Colony Optimization Clustering Algorithm in Image Segmentation

Journal from gdlhub / 2016-11-17 03:18:08
Oleh : Junhui Zhou, Defa Hu, Telkomnika
Dibuat : 2015-09-01, dengan 1 file

Keyword : ant colony optimization, clustering algorithm, image segmentation
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1803

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.

Deskripsi Alternatif :

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.


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