Joint Data Filtering and Labeling Using Gaussian Processes and Alternating Direction Method of Multipliers

Ruiz, Pablo; Molina, Rafael; Katsaggelos, Aggelos K.

doi:10.1109/TIP.2016.2558472

Title: Joint Data Filtering and Labeling Using Gaussian Processes and Alternating Direction Method of Multipliers

Journal Article · Tue Apr 26 00:00:00 EDT 2016 · IEEE Transactions on Image Processing

DOI:https://doi.org/10.1109/TIP.2016.2558472· OSTI ID:1487461

^[1]; Molina, Rafael ^[1];

^[2]

Univ. de Granada, Granada (Spain)
Northwestern Univ., Evanston, IL (United States)

Here, 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.

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Research Organization:: Northwestern Univ., Evanston, IL (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)

DOE Contract Number:: NA0002520

OSTI ID:: 1487461

Journal Information:: IEEE Transactions on Image Processing, Vol. 25, Issue 7; ISSN 1057-7149

Publisher:: IEEE

Country of Publication:: United States

Language:: English

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