• Jyoti Pandey

Image segmentation is a complex visual computation problem, which refers the process of distinguishing objects from background. Thresholding techniques is used for finding optimal thresholds between the various object and background. K-means is very popular clustering algorithm; it is most wide and effective in field of image segmentation. According to the characteristics of the ant colony optimization and the K-means clustering, a method for the image segmentation based on the ant colony optimization and the Kmeans clustering was proposed in this paper. Firstly, the basic principles of the two algorithms were introduced. Secondly, their characteristics on the image segmentation were analyzed. Finally the improved algorithm was proposed, this algorithm can effectively overcome shortages which are the slow rate of the ant colony optimization and the K-means clustering dependent on the initial clustering centers. Experimental results proved that the improved algorithm was an effective method for the image segmentation In the practical application, which could segment the object accurately.

ACO, TCP

"K-Mean and Ant Colony Optimization Techniques for Digital Image Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.b748-b751, April-2022, Available :http://www.jetir.org/papers/JETIR2204192.pdf

"K-Mean and Ant Colony Optimization Techniques for Digital Image Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppb748-b751, April-2022, Available at : http://www.jetir.org/papers/JETIR2204192.pdf