Fully-connected CRFs with non-parametric pairwise potential

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

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

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
Pages (from-to)1658-1665
Number of pages8
JournalIEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
Publication statusPublished - 1 Jan 2013

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