Nonparametric Bayesian Texture Learning and Synthesis
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Torralba_Nonparametric Bayesian.pdf
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2.95 MB
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Author(s)
Zhu, Long
Chen, Yuanhao
Freeman, William T.
Torralba, Antonio
Date Issued
December 2009
Journal
Papers of the Twenty-Third Annual Conference on Neural Information Processing Systems, NIPS 2009
Publisher
Neural Information Processing Systems Foundation
Citation
Zhu, Long (Leo) et al. "Nonparametric Bayesian Texture Learning and Synthesis." in Accepted Papers of the Twenty-Third Annual Conference on Neural Information Processing Systems, NIPS 2009, December 7-10, 2009, Hyatt Regency, Vancouver, BC, Canada.
Version
Author's final manuscript
Abstract
We present a nonparametric Bayesian method for texture learning and synthesis.
A texture image is represented by a 2D Hidden Markov Model (2DHMM) where
the hidden states correspond to the cluster labeling of textons and the transition
matrix encodes their spatial layout (the compatibility between adjacent textons).
The 2DHMM is coupled with the Hierarchical Dirichlet process (HDP) which allows
the number of textons and the complexity of transition matrix grow as the
input texture becomes irregular. The HDP makes use of Dirichlet process prior
which favors regular textures by penalizing the model complexity. This framework
(HDP-2DHMM) learns the texton vocabulary and their spatial layout jointly
and automatically. The HDP-2DHMM results in a compact representation of textures
which allows fast texture synthesis with comparable rendering quality over
the state-of-the-art patch-based rendering methods. We also show that the HDP-
2DHMM can be applied to perform image segmentation and synthesis. The preliminary
results suggest that HDP-2DHMM is generally useful for further applications
in low-level vision problems.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
http://books.nips.cc/papers/files/nips22/NIPS2009_0173.pdf
