An image segmentation method, comprising: establishing (101,201) a saliency model of an image; automatically acquiring a foreground sample point and a background sample point of the image according to the saliency model; establishing a foreground and background classification model according to the saliency model as well as the foreground sample point and the background sample point; segmenting the image according to a predefined graph cut algorithm which segments the image by using the foreground and background classification model and edge information related to pixels, by; calculating a foreground similarity of each pixel in the image by using the foreground classification model; calculating a background similarity of each pixel in the image by using the background classification model; acquiring a similarity between adjacent pixels in the image; constructing an undirected graph required for the predefined graph cut algorithm by using a foreground similarity of each pixel, a background similarity of each pixel and a similarity between adjacent pixels; and segmenting the undirected graph by using the predefined graph cut algorithm, thus completing segmentation of the image; characterized in that said constructing comprises; constructing an undirected graph comprising a foreground vertex and a background vertex, wherein, a first boundary is formed by connection of pixel vertexes, a second boundary is formed by connection of the foreground vertex with any pixel vertex, and a third boundary is formed by connection of the background vertex with any pixel vertex; with regard to each second boundary, determining the foreground similarity of a pixel corresponding to a pixel vertex connected to the second boundary as a weight value of the second boundary; with regard to each third boundary, determining the background similarity of a pixel corresponding to a pixel vertex connected to the third boundary as a weight value of the third boundary; and with regard to each first boundary, determining the similarity between two pixels corresponding to two pixel vertexes connected to the first boundary as a weight value of the first boundary. establishing (101,201) a saliency model of an image; automatically acquiring a foreground sample point and a background sample point of the image according to the saliency model; establishing a foreground and background classification model according to the saliency model as well as the foreground sample point and the background sample point; segmenting the image according to a predefined graph cut algorithm which segments the image by using the foreground and background classification model and edge information related to pixels, by; calculating a foreground similarity of each pixel in the image by using the foreground classification model; calculating a background similarity of each pixel in the image by using the background classification model; acquiring a similarity between adjacent pixels in the image; constructing an undirected graph required for the predefined graph cut algorithm by using a foreground similarity of each pixel, a background similarity of each pixel and a similarity between adjacent pixels; and segmenting the undirected graph by using the predefined graph cut algorithm, thus completing segmentation of the image; characterized in that said constructing comprises; constructing an undirected graph comprising a foreground vertex and a background vertex, wherein, a first boundary is formed by connection of pixel vertexes, a second boundary is formed by connection of the foreground vertex with any pixel vertex, and a third boundary is formed by connection of the background vertex with any pixel vertex; with regard to each second boundary, determining the foreground similarity of a pixel corresponding to a pixel vertex connected to the second boundary as a weight value of the second boundary; with regard to each third boundary, determining the background similarity of a pixel corresponding to a pixel vertex connected to the third boundary as a weight value of the third boundary; and with regard to each first boundary, determining the similarity between two pixels corresponding to two pixel vertexes connected to the first boundary as a weight value of the first boundary.