Title

Learning Optimized Map Estimates In Continuously-Valued Mrf Models

Abstract

We present a new approach for the discriminative training of continuous-valued Markov Random Field (MRF) model parameters. In our approach we train the MRF model by optimizing the parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth. This leads to parameters which are directly optimized to increase the quality of the MAP estimates during inference. Our proposed technique allows us to develop a framework that is flexible and intuitively easy to understand and implement, which makes it an attractive alternative to learn the parameters of a continuous-valued MRF model. We demonstrate the effectiveness of our technique by applying it to the problems of image denoising and inpainting using the Field of Experts model. In our experiments, the performance of our system compares favourably to the Field of Experts model trained using contrastive divergence when applied to the denoising and inpainting tasks. © 2009 IEEE.

Publication Date

1-1-2009

Publication Title

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Number of Pages

477-484

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPRW.2009.5206774

Socpus ID

70450207702 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/70450207702

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