Image segmentation method based on generalized data field and Normalized cut (Ncut) algorithm

The invention provides an image segmentation method based on a generalized data field and an Ncut algorithm. Characteristic space is divided into hierarchical grids including a first layer of grids and a second layer of grids, corresponding grid characteristic space omega s and omega b is formed simultaneously, and adjacent eight small grids of the fist layer form a large grid of the second layer; and then potential value distribution of the first layer of the grids is calculated and obtained by using a grouped dynamic frame (GDF) algorithm based on the second layer of grids. Based on the potential value distribution, the first layer of the grids are clustered, clustered results are mapped to an image, and accordingly, initial segmentation operation on the image is achieved, and the image is divided into different areas which are not mutually intersected; and finally, undirected weighted image is constructed based on the initial segmentation results of the image, and homogenous areas are combined through the Ncut algorithm based on the areas until best image segmentation results are achieved. The image segmentation method has the advantages of being rapid, simple and accurate in image segmentation.