NDLI: Building a better probabilistic model of images by factorization

Content Provider	IEEE Xplore Digital Library
Author	Culpepper, B.J. Sohl-Dickstein, J. Olshausen, B.A.
Copyright Year	2011
Description	Author affiliation: UC Berkeley, CA, USA (Culpepper, B.J.; Sohl-Dickstein, J.; Olshausen, B.A.)
Abstract	We describe a directed bilinear model that learns higher-order groupings among features of natural images. The model represents images in terms of two sets of latent variables: one set of variables represents which feature groups are active, while the other specifies the relative activity within groups. Such a factorized representation is beneficial because it is stable in response to small variations in the placement of features while still preserving information about relative spatial relationships. When trained on MNIST digits, the resulting representation provides state of the art performance in classification using a simple classifier. When trained on natural images, the model learns to group features according to proximity in position, orientation, and scale. The model achieves high log-likelihood (−94 nats), surpassing the current state of the art for natural images achievable with an mcRBM model.
Starting Page	2011
Ending Page	2017
File Size	667306
Page Count	7
File Format	PDF
ISBN	9781457711015
ISSN	15505499
e-ISBN	9781457711022
DOI	10.1109/ICCV.2011.6126473
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Training Data models Computational modeling Kernel Accuracy Vectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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