Learning visual object categories from few training examples
Name
755604510-MIT.pdf
Description
Full printable version
Size
13.84 MB
Format
Adobe PDF
Checksum (MD5)
1bac2f3ec16430f59031661d08508304
Author(s)
Kuo, Michael
Advisor(s)
Predrag Neskovic and Antonio Torralba.
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
During visual perception of complex objects, humans fixate on salient regions of a particular object, moving their gaze from one region to another in order to gain information about that object. The Bayesian Integrate and Shift (BIAS) model is a recently proposed model for learning visual object categories that is modeled after the process of human visual perception, integrating information from within and across fixations. Previous works have described preliminary evaluations of the BIAS model and demonstrated that it can learn new object categories from only a few examples. In this thesis, we introduce and evaluate improvements to the learning algorithm, demonstrate that the model benefits from using information from fixating on multiple regions of a particular object, evaluate the limitations of the model when learning different object categories, and assess the performance of the learning algorithm when objects are partially occluded.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 73-74).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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reproduction or distribution in any format is prohibited without written
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