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Unsupervised learning of models for object recognition

Citation

Weber, Markus (2000) Unsupervised learning of models for object recognition. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:10052010-115540388

Abstract

A method is presented to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. The variability across a class of objects is modeled in a principled way, treating objects as flexible constellations of rigid parts (features). Variability is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. Corresponding "constellation models" can be learned in a completely unsupervised fashion. In a first stage, the learning method automatically identifies distinctive parts in the training set by applying a clustering algorithm to patterns selected by an interest operator. It then learns the statistical shape model using expectation maximization. Mixtures of constellation models can be defined and applied to "discover" object categories in an unsupervised manner. The method achieves very good classification results on human faces, cars, leaves, handwritten letters, and cartoon characters.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Computation and Neural Systems
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computation and Neural Systems
Thesis Availability:Restricted to Caltech community only
Research Advisor(s):
  • Perona, Pietro
Thesis Committee:
  • Unknown, Unknown
Defense Date:1 May 2000
Record Number:CaltechTHESIS:10052010-115540388
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:10052010-115540388
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:6095
Collection:CaltechTHESIS
Deposited By: Dan Anguka
Deposited On:05 Oct 2010 20:07
Last Modified:26 Dec 2012 04:30

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