A biological model of object recognition with feature learning
Name
53834027-MIT.pdf
Description
Full printable version
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6.05 MB
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ebd7961ef68b798067328a7e774750d6
Author(s)
Louie, Jennifer, 1980-
Advisor(s)
Tomaso Poggio.
Date Issued
2003
Publisher
Massachusetts Institute of Technology
Abstract
Previous biological models of object recognition in cortex have been evaluated using idealized scenes and have hard-coded features, such as the HMAX model by Riesenhuber and Poggio [10]. Because HMAX uses the same set of features for all object classes, it does not perform well in the task of detecting a target object in clutter. This thesis presents a new model that integrates learning of object-specific features with the HMAX. The new model performs better than the standard HMAX and comparably to a computer vision system on face detection. Results from experimenting with unsupervised learning of features and the use of a biologically-plausible classifier are presented.
Description
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaves 67-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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