Graph Cuts-Based Automatic Color Image Segmentation

A graph cuts method has recently attracted a lot of attention for image segmentation, as it can minimize an energy function composed of data term estimated in feature space and smoothness term estimated in an image domain. Although previous approaches using graph cuts have shown good performance for image segmentation, they manually obtained prior information to estimate the data term, thus automatic image segmentation is one of issues in application using the graph cuts method. To automatically estimate the data term, GMM (Gaussian mixture model) is generally used, but it is practicable only for classes with a hyper-spherical or hyper-ellipsoidal shape, as the class was represented based on the covariance matrix centered on the mean. For arbitrary-shaped classes, this paper proposes graph cuts-based image segmentation using mean shift analysis. As prior information to estimate the data term, we use the set of mean trajectories toward each mode from initial means randomly selected in L*u*v* feature space. Since the mean shift procedure requires many computational times, we transform features in continuous feature space into 3D discrete grid, and use 3D kernel based on the first moment in the grid, which are needed to move the means to modes. In the experiments, we investigated problems of normalized cuts-based and mean shift-based segmentation and graph cuts-based segmentation using GMM. As a result, the proposed method showed better performance than previous three methods on Berkeley segmentation dataset.