Applications of Improved Ant Colony Optimization Clustering Algorithm in Image Segmentation
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.
Author: Junhui Zhou, Defa Hu
Journal Code: jptkomputergg150124
