ABSTRACT Title of dissertation: LEARNING VISUAL PATTERNS: IMPOSING ORDER ON OBJECTS, TRAJECTORIES AND NETWORKS

Fundamental to many tasks in the field of computer vision, this work con-siders the understanding of observed visual patterns in static images and dynamic scenes. Within this broad domain, we focus on three particular subtasks, contribut-ing novel solutions to: (a) the subordinate categorization of objects (avian species specifically), (b) the analysis of multi-agent interactions using the agent trajectories, and (c) the estimation of camera network topology. In contrast to object recognition, where the presence or absence of certain parts is generally indicative of basic-level category, the problem of subordinate cat-egorization rests on the ability to establish salient distinctions amongst the char-acteristics of those parts which comprise the basic-level category. Focusing on an avian domain due to the fine-grained structure of the category taxonomy, we ex-plore a pose-normalized appearance model based on a volumetric poselet scheme. The variation in shape and appearance properties of these parts across a taxonomy provides the cues needed for subordinate categorization. Our model associates the underlying image pattern parameters used for detection with corresponding volu