Learning Appearance Based Models: Hierarchical Mixtures of Experts Approach Based on Generalized Second Moments
Christoph Bregler and Jitendra Malik
EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-96-897
1995
http://www.eecs.berkeley.edu/Pubs/TechRpts/1996/CSD-96-897.pdf
This paper describes a new technique for object recognition based on learning appearance models. The image is decomposed into local regions which are described by a new texture representation derived from the output of multiscale, multiorientation filter banks. We call this representation "Generalized Second Moments" as it can be viewed as a generalization of the windowed second moment matrix representation used by Garding & Lindeberg. Class-characteristic local texture features and their global composition is learned by a hierarchical mixture of experts architecture. The technique is applied to a vehicle database consisting of 5 general car categories (Sedan, Van with back-doors, Van without back-doors, old Sedan, and Volkswagen Bug). This is a difficult problem with considerable in-class variation. Our technique has a 6.5% misclassification rate, compared to eigen-images which give 17.4% misclassification rate, and nearest neighbors which give 15.7% misclassification rate.
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BibTeX citation:
@techreport{Bregler:CSD-96-897, Author = {Bregler, Christoph and Malik, Jitendra}, Title = {Learning Appearance Based Models: Hierarchical Mixtures of Experts Approach Based on Generalized Second Moments}, Institution = {EECS Department, University of California, Berkeley}, Year = {1995}, Month = {Nov}, URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/1995/5360.html}, Number = {UCB/CSD-96-897} }
EndNote citation:
%0 Report %A Bregler, Christoph %A Malik, Jitendra %T Learning Appearance Based Models: Hierarchical Mixtures of Experts Approach Based on Generalized Second Moments %I EECS Department, University of California, Berkeley %D 1995 %@ UCB/CSD-96-897 %U http://www.eecs.berkeley.edu/Pubs/TechRpts/1995/5360.html %F Bregler:CSD-96-897