The problem of image segmentation is formulated in terms of recursive partitioning of segments into subsegments by optimizing the proposed objective function via graph cuts. Our approach uses a special normalization of the objective function, which enables the production of a hierarchy of regular superpixels that adhere to image boundaries. To enforce compactness and visual homogeneity of segments a regularization strategy is proposed. Experiments on the Berkeley dataset show that the proposed algorithm is comparable in its performance to the state-of-the-art superpixel methods. 
A graph based approach to hierarchical image over-segmentation