STRUCTURED GRAPHICAL MODELS FOR UNSUPERVISED IMAGE SEGMENTATION By KITTIPAT KAMPA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy STRUCTURED GRAPHICAL MODELS FOR UNSUPERVISED IMAGE SEGMENTATION By Kittipat Kampa Dec 2011 Chair: Jose C. Principe Major: Electrical and Computer Engineering 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.