Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations

author: Mingyuan Zhou, Department of Electrical and Computer Engineering, Duke University
published: Jan. 19, 2010,   recorded: December 2009,   views: 8276


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Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainting and compressive sensing (CS). The beta process is employed as a prior for learning the dictionary, and this non-parametric method naturally infers an appropriate dictionary size. The Dirichlet process and a probit stick-breaking process are also considered to exploit structure within an image. The proposed method can learn a sparse dictionary in situ; training images may be exploited if available, but they are not required. Further, the noise variance need not be known, and can be non-stationary. Another virtue of the proposed method is that sequential inference can be readily employed, thereby allowing scaling to large images. Several example results are presented, using both Gibbs and variational Bayesian inference, with comparisons to other state-of-the-art approaches.

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Reviews and comments:

Comment1 Jianquan Mao, January 26, 2010 at 2:31 a.m.:

Such a great speech!

Comment2 Jincheng Pang, January 26, 2010 at 8:04 p.m.:

Excellent speech

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