Clustering Approach Towards Image Segmentation: An Analytical Study
This is an incremental study that reviews and applies existing methods to image segmentation, a key problem in computer vision for researchers and practitioners.
The paper conducted an analytical study on clustering techniques for image segmentation, evaluating their pros and cons, and performed an experiment using the K-Means algorithm on color images to observe segmentation accuracy.
Image processing is an important research area in computer vision. Image segmentation plays the vital rule in image processing research. There exist so many methods for image segmentation. Clustering is an unsupervised study. Clustering can also be used for image segmentation. In this paper, an in-depth study is done on different clustering techniques that can be used for image segmentation with their pros and cons. An experiment for color image segmentation based on clustering with K-Means algorithm is performed to observe the accuracy of clustering technique for the segmentation purpose.