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Part-Based Statistical Models for Object Classification and Detection Elliot Joel Bernstein and Yali Amit
 

Summary: Part-Based Statistical Models for Object Classification and Detection
Elliot Joel Bernstein and Yali Amit
Department of Statistics, University of Chicago
E-mail: {bernstei,amit}@galton.uchicago.edu
Abstract
We propose using simple mixture models to define a set
of mid-level binary local features based on binary oriented
edge input. The features capture natural local structures
in the data and yield very high classification rates when
used with a variety of classifiers trained on small train-
ing sets, exhibiting robustness to degradation with clutter.
Of particular interest are the use of the features as vari-
ables in simple statistical models for the objects thus en-
abling likelihood based classification. Pre-training deci-
sion boundaries between classes, a necessary component of
non-parametric techniques, is thus avoided. Class models
are trained separately with no need to access data of other
classes. Experimental results are presented for handwrit-
ten character recognition, classification of deformed LATEX
symbols involving hundreds of classes, and side view car

  

Source: Amit, Yali - Departments of Computer Science & Statistics, University of Chicago

 

Collections: Computer Technologies and Information Sciences