Study On Region Based Image Segmentation Methods

Image segmentation is a key problem in computer vision. The region-based image segmentation method is studied in this paper. In methodology, three aspects are made research: watershed translation based image segmentation method, kernel clustering and graph theory based image segmentation technique. Two kinds of segmentation algorithm are proposed: image segmentation based on watershed transform, kernel clustering and image segmentation approach by combining watershed translation and graph theory.An image is regarded as a topographic surface. Gray values denote altitude. Watershed translation is the process that the surface is immersed in the water. Watershed algorithm provides the advantages of stabilization and speediness, but is prone to over-segmentation. Clustering algorithm is well-known segmentation method in image project. Considering large quantity data of image, classical clustering algorithms are poor in speed and accurateness.Kernel clustering algorithm is used to merging small partition separated by watershed translation in the image segmentation method based on watershed transform and kernel clustering. Kernel clustering maps the data in the original space to a high-dimensional feature space by using mercer kernel function, and optimizes the initial data. There are great improvements in performance compared with classical clustering algorithms.There has been an increasing interest in graph-theoretic segmentation algorithms based on clustering recently. An image is represented as a similarity edge-weighted graph, where the vertices represent individual pixels. The method is rather time-consuming with an increase in pixels. Small partitions are considered as vertices of an undirected weighted graph. Integrating gray feature and spatial location of each partition, normalized cut is used to segment between partitions from global view, and then produces the final segmented image. It is an effective image segmentation approach.