On TV Regularization of Image Flows and MRF-Inference by DC-Programming

Christoph Schnörr
University of Heidelberg

The talk addresses two different topics of continuous and discrete optimization.
First, an extension of the ROF-model to vector-valued data (image
flows) is considered. The resulting regularizer differs from the commonly used term and gives rise to a decomposition into piecewise harmonic flow structure as opposed to piecewise constant flows, and motion texture.

The second part of the talk considers the MAP-inference problem and a QP-relaxation recently suggested by Ravikumar and Lafferty. As an alternative to rectifying the spectrum of the corresponding nonconvex quadratic forms, we study a class of convergent inference algorithms based on DC-programming. Experiments indicate that this alternative can be beneficial for highly-connected graphs with large state-spaces.

Christoph Schnoerr, University of Heidelberg Joint work with Jing Yuan (part 1) and Joerg Kappes (part 2)

Presentation (PDF File)

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