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A Probabilistic Technique For Open Set Recognition Using Support Vector Machines

Scherreik, Matthew
http://rave.ohiolink.edu/etdc/view?acc_num=wright1419252745

Abstract Details

2014, Master of Science in Engineering (MSEgr), Wright State University, Electrical Engineering.
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.
Brian Rigling, Ph.D. (Advisor)
Fred Garber, Ph.D. (Committee Member)
Arnab Shaw, Ph.D. (Committee Member)
74 p.
Electrical Engineering
Open Set Recognition, POS-SVM, Machine Learning, Ladar, FLIR, Automatic Target Recognition, Support Vector Machines, Signal Processing

Recommended Citations

Citations

  • Scherreik, M. (2014). A Probabilistic Technique For Open Set Recognition Using Support Vector Machines [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1419252745

    APA Style (7th edition)

  • Scherreik, Matthew. A Probabilistic Technique For Open Set Recognition Using Support Vector Machines. 2014. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1419252745.

    MLA Style (8th edition)

  • Scherreik, Matthew. "A Probabilistic Technique For Open Set Recognition Using Support Vector Machines." Master's thesis, Wright State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1419252745

    Chicago Manual of Style (17th edition)

wright1419252745
1,118
© 2014, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.