Data-driven tree-structured Bayesian network for image segmentation

This paper presents Data-Driven Tree-structured Bayesian network (DDT), a novel probabilistic graphical model for hierarchical unsupervised image segmentation. The DDT captures long and short-ranged correlations between neighboring regions in each image using a tree-structured prior. Unlike other previous work, DDT first segments an input image into superpixels and learn a tree-structured prior based on the topology of superpixels in different scales. Such a tree structure is referred to as data-driven tree structure. Each superpixel is represented by a variable node taking a discrete value of class/label of the segmentation. The probabilistic relationships among the nodes are represented by edges in the network. The unsupervised image segmentation, hence, can be viewed as an inference problem of the nodes in the tree structure of DDT, which can be carried out efficiently. We evaluate quantitatively our results with respect to the ground-truth segmentation, demonstrating that our proposed framework performs competitively with the state of the art in unsupervised image segmentation and contour detection.