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Unsupervised Learning of Individuals and Categories from Images

Waydo, Stephen and Koch, Christof (2008) Unsupervised Learning of Individuals and Categories from Images. Neural Computation, 20 (5). pp. 1165-1178. ISSN 0899-7667. doi:10.1162/neco.2007.03-07-493.

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Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data.

Item Type:Article
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URLURL TypeDescription
Koch, Christof0000-0001-6482-8067
Additional Information:© 2008 The MIT Press. Received March 20, 2007; accepted July 16, 2007. This work was supported by grants from NIMH, NSF, ONR, DARPA, the Mathers Foundation, and a Fannie and John Hertz Foundation fellowship to S.W. Thomas Serre and Minjoon Kouh of MIT provided invaluable assistance in the setup and operation of the underlying vision model.We thank Richard Murray, Pietro Perona, Jerry Marsden, Tomoso Poggio, Bruno Olshausen, and the members of klab for valuable comments on this work.
Group:Koch Laboratory (KLAB)
Funding AgencyGrant Number
U.S. Office of Naval ResearchUNSPECIFIED
Mathers FoundationUNSPECIFIED
Fannie and John Hertz FoundationUNSPECIFIED
Issue or Number:5
Record Number:CaltechAUTHORS:WAYnc08
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10423
Deposited By: Archive Administrator
Deposited On:04 May 2008
Last Modified:08 Nov 2021 21:07

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