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My dissertation presents several new algorithms incorporating non-parametric and deep learning approaches for computer vision and related tasks, including object localization, object tracking and model compression. With respect to object localization, I introduce a method to perform active localization by modeling spatial and other relationships between objects in a coherent “visual situation” using a set of probability distributions. I further refine this approach with the Multipole Density Estimation with Importance Clustering (MICSituate) algorithm. Next, I formulate active, “situation” object search as a Bayesian optimization problem using Gaussian Processes. Using my Gaussian Process ContextSituation Learning (GP-CL) algorithm, I demonstrate improved efficiency for object localization over baseline procedures. In subsequent work, I expand this research to frame object tracking in video as a temporally-evolving, dynamic Bayesian optimization problem. Here I present the Siamese-Dynamic Bayesian Tracking Algorithm (SDBTA), the first integrated dynamic Bayesian optimization framework in combination with deep learning for video tracking. Through experiments, I show improved results for video tracking in comparison with baseline approaches. Finally, I propose a novel data compression algorithm, Regularized L21 Semi-NonNegative Matrix Factorization (L21 SNF) which serves as a general purpose, parts-based compression algorithm, applicable to deep model compression.

Leveraging Model Flexibility and Deep Structure: Non-Parametric and Deep Models for Computer Vision Processes with Applications to Deep Model Compression