Generalized Forgy’s Algotithm for Efficient Image Segmentation

Abstract: The use of the conventional watershed algorithm for different image analysis is widespread, such as always being able to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. We proposed a methodology which incorporates Forgy’s and improved watershed segmentation algorithm for image segmentation. The Forgy’s algorithm is an unsupervised learning algorithm, while the improved watershed algorithm makes the use of automated thresholding on the gradient magnitude map and post- segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. By comparing the number of partitions in the segmentation maps of images, we showed that our proposed methodology produced segmentation maps which have 92% fewer partitions than the segmentation maps produced by the conventional watershed algorithm.

Keywords: Thresholding, Segmentation, Clustering and Watershed algorithm.