Joint data filtering and labeling using Gaussian processes and alternating direction method of multipliers

Pablo Ruiz, Rafael Molina, Aggelos K Katsaggelos

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Sequence labeling aims at assigning a label to every sample of a signal (or pixel of an image) while considering the sequentiality (or vicinity) of the samples. To perform this task, many works in the literature first filter and then label the data. Unfortunately, the filtering, which is performed independently from the labeling, is far from optimal and frequently makes the latter task harder. In this paper, a novel approach that trains a Gaussian process classifier and estimates the coefficients of an optimal filter jointly is presented. The new approach, based on Bayesian modeling and alternating direction method of multipliers (ADMMs) optimization, performs both tasks simultaneously. All unknowns are treated as stochastic variables, which are estimated using variational inference and filtering and labeling are linked with the use of ADMM. In the experimental section, synthetic and real experiments are presented to compare the proposed method with other existing approaches.

Original languageEnglish (US)
Article number7460233
Pages (from-to)3059-3072
Number of pages14
JournalIEEE Transactions on Image Processing
Volume25
Issue number7
DOIs
StatePublished - Jul 1 2016

Keywords

  • ADMM
  • Bayesian Modeling
  • Classification
  • Filtering
  • Gaussian Processes
  • Variational Inference

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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