Structured graphical models for unsupervised image segmentation

In the dissertation, we seek the following goals: (1) to come up with a probabilistic graphical model framework for unsupervised segmentation on structured data, and (2) to find a computationally efficient and reliable solution to image segmentation with superpixels as opposed to pixels.;We develop a Data-Driven Tree-structured Bayesian network (DDT), a novel probabilistic graphical model for hierarchical unsupervised image segmentation. Like tree-structure belief networks (TSBNs), DDT captures both long and short-ranged correlations between neighboring regions in each image using a tree-structured prior. Unlike other approaches, DDT first segments an input image into superpixels and learns a tree-structured prior based on the topology of superpixels in different scales. Such a tree structure is referred to as a data-driven tree structure. Each superpixel is represented by a variable node taking a discrete value of segmentation class/label. The probabilistic relationships among the nodes are represented by edges in the network. Hence, unsupervised image segmentation can be viewed as an inference problem on the DDT structure nodes, which can be carried out efficiently. The end image segmentation result can be obtained by applying the maximum posterior marginal to each variable node in the network. We provide the parameter estimation regime using the Expectation-Maximization (EM) algorithm combined with the sum-product algorithm.;With respect to the objectives, we hypothesize that 1) Hierarchical segmentation gives more meaningful results than the results from only one scale; 2) The tree-structure prior would smooth the segmentation results, yielding better segmentation; 3) Exploiting the superpixel would be a way to smooth the segmentation, so the segmentation differences between the model with and without the tree-structured prior would be less at the superpixel-level segmentation than the pixel-level; 4) The model with evidence in all scales gives better results than the one without.;We evaluate quantitatively our results with respect to the ground-truth segmentation in the Berkeley Segmentation Dataset and Benchmark 500 (BSDS500), a well-known image database benchmark, demonstrating that our proposed framework performs competitively with the state of the art in unsupervised image segmentation and contour detection. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html).