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Unsupervised Learning of Models for Recognition

Weber, M. and Welling, M. and Perona, P. (2000) Unsupervised Learning of Models for Recognition. In: Computer Vision - ECCV 2000. Lecture Notes in Computer Science. No.1842. Springer , Berlin, Heidelberg, pp. 18-32. ISBN 9783540676850.

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We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of model where objects are represented as flexible constellations of rigid parts (features). The variability within a class is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. In a first stage, the 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. The method achieves very good classification results on human faces and rear views of cars.

Item Type:Book Section
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URLURL TypeDescription ReadCube access
Perona, P.0000-0002-7583-5809
Additional Information:© Springer-Verlag Berlin Heidelberg 2000. This work was funded by the NSF Engineering Research Center for Neuromorphic Systems Engineering (CNSE) at Caltech (NSF9402726), and an NSF National Young Investigator Award to P.P. (NSF9457618). M.Welling was supported by the Sloan Foundation. We are also very grateful to Rob Fergus for helping with collecting the databases and to Thomas Leung, Mike Burl, Jitendra Malik and David Forsyth for many helpful comments.
Funding AgencyGrant Number
Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
Alfred P. Sloan FoundationUNSPECIFIED
Subject Keywords:Expectation Maximization; Training Image; Object Class; Unsupervised Learn; Expectation Maximization Algorithm
Series Name:Lecture Notes in Computer Science
Issue or Number:1842
Record Number:CaltechAUTHORS:20190829-131534540
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98356
Deposited By: George Porter
Deposited On:30 Aug 2019 15:52
Last Modified:16 Nov 2021 17:38

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