Publication Date
2014
Document Type
Thesis
Committee Members
Brian Rigling, Ph.D. (Advisor); Fred Garber, Ph.D. (Committee Member); Arnab Shaw, Ph.D. (Committee Member)
Degree Name
Master of Science in Engineering (MSEgr)
Abstract
Classification algorithms trained using finite sets of target and confuser data are limited by the training set. These algorithms are trained under closed set assumptions and do not account for the infinite universe of confusers found in practice. In contrast, classification algorithms developed under open set assumptions label inputs not present in the training data as unknown instead of assigning the most likely class. We present an approach to open set recognition, the probabilistic open set SVM, that utilizes class posterior estimates to determine probability thresholds for classification. This is accomplished by first training an SVM in a 1-vs-all configuration on a training dataset containing only target classes. A validation set containing only class data belonging to the training set is used to iteratively determine appropriate posterior probability thresholds for each target class. The testing dataset, which contains targets present in the training data as well as several confuser classes, is first classified by the 1-vs-all SVM. If the estimated posterior for an input falls below the threshold, the target is labeled as unknown. Otherwise, it is labeled with the class resulting from the SVM decision. We apply our method to classification of synthetic ladar range images of civilian vehicles and measured infrared images of military vehicles. We show that the POS-SVM offers improved performance over other open set algorithms by allowing the use of nonlinear kernels, incorporating intuitive free parameters, and empirically determining good thresholds.
Page Count
74
Department or Program
Department of Electrical Engineering
Year Degree Awarded
2014
Copyright
Copyright 2014, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
