Fully-Connected CRFs with Non-Parametric Pairwise Potential

N. D. F. Campbell, K. Subr, J. Kautz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Conditional Random Fields (CRFs) are used for diverse tasks, ranging from image denoising to object recognition. For images, they are commonly defined as a graph with nodes corresponding to individual pixels and pairwise links that connect nodes to their immediate neighbors. Recent work has shown that fully-connected CRFs, where each node is connected to every other node, can be solved efficiently under the restriction that the pairwise term is a Gaussian kernel over a Euclidean feature space. In this paper, we generalize the pairwise terms to a non-linear dissimilarity measure that is not required to be a distance metric. To this end, we propose a density estimation technique to derive conditional pairwise potentials in a non-parametric manner. We then use an efficient embedding technique to estimate an approximate Euclidean feature space for these potentials, in which the pairwise term can still be expressed as a Gaussian kernel. We demonstrate that the use of non-parametric models for the pairwise interactions, conditioned on the input data, greatly increases expressive power whilst maintaining efficient inference.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1658-1665
Number of pages8
ISBN (Electronic)978-0-7695-4989-7
DOIs
Publication statusPublished - Jun 2013

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