Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques

Yun Zeng, Chaohui Wang, Stefano Soatto, Shing-Tung Yau; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1706-1713

Abstract


This paper introduces an efficient approach to integrating non-local statistics into the higher-order Markov Random Fields (MRFs) framework. Motivated by the observation that many non-local statistics (e.g., shape priors, color distributions) can usually be represented by a small number of parameters, we reformulate the higher-order MRF model by introducing additional latent variables to represent the intrinsic dimensions of the higher-order cliques. The resulting new model, called NC-MRF, not only provides the flexibility in representing the configurations of higher-order cliques, but also automatically decomposes the energy function into less coupled terms, allowing us to design an efficient algorithmic framework for maximum a posteriori (MAP) inference. Based on this novel modeling/inference framework, we achieve state-of-the-art solutions to the challenging problems of class-specific image segmentation and template-based 3D facial expression tracking, which demonstrate the potential of our approach.

Related Material


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[bibtex]
@InProceedings{Zeng_2013_CVPR,
author = {Zeng, Yun and Wang, Chaohui and Soatto, Stefano and Yau, Shing-Tung},
title = {Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}