NDLI: An empirical Bayes approach to contextual region classification

Content Provider	IEEE Xplore Digital Library
Author	Lazebnik, S. Raginsky, M.
Copyright Year	2009
Description	Author affiliation: Univ. of North Carolina, Chapel Hill, NC, USA (Lazebnik, S.) \|\| Duke Univ., Durham, NC, USA (Raginsky, M.)
Abstract	This paper presents a nonparametric approach to labeling of local image regions that is inspired by recent developments in information-theoretic denoising. The chief novelty of this approach rests in its ability to derive an unsupervised contextual prior over image classes from unlabeled test data. Labeled training data is needed only to learn a local appearance model for image patches (although additional supervisory information can optionally be incorporated when it is available). Instead of assuming a parametric prior such as a Markov random field for the class labels, the proposed approach uses the empirical Bayes technique of statistical inversion to recover a contextual model directly from the test data, either as a spatially varying or as a globally constant prior distribution over the classes in the image. Results on two challenging datasets convincingly demonstrate that useful contextual information can indeed be learned from unlabeled data.
Starting Page	2380
Ending Page	2387
File Size	2072254
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Higher order statistics Labeling Layout Image restoration Belief propagation Optimization methods Iterative algorithms Biology Energy capture
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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