Sponsor
Portland State University. Department of Mathematics and Statistics
First Advisor
Bin Jiang
Term of Graduation
Spring 2020
Date of Publication
5-12-2020
Document Type
Dissertation
Degree Name
Doctor of Philosophy (Ph.D.) in Mathematical Sciences
Department
Mathematics and Statistics
Language
English
Subjects
Computer vision, Machine learning, Computer algorithms
DOI
10.15760/etd.7320
Physical Description
1 online resource (xi, 94 pages)
Abstract
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 (MIC-Situate) algorithm. Next, I formulate active, "situation" object search as a Bayesian optimization problem using Gaussian Processes. Using my Gaussian Process Context Situation 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.
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Persistent Identifier
https://archives.pdx.edu/ds/psu/33240
Recommended Citation
Rhodes, Anthony D., "Leveraging Model Flexibility and Deep Structure: Non-Parametric and Deep Models for Computer Vision Processes with Applications to Deep Model Compression" (2020). Dissertations and Theses. Paper 5447.
https://doi.org/10.15760/etd.7320