Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Co-training with Noisy Perceptual Observations

C. Mario Christhoudias, Raquel Urtasun, Ashish Kapoor and Trevor Darrell

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2009-17
January 27, 2009

http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-17.pdf

Many perception and multimedia indexing problems involve datasets that are naturally comprised of multiple streams or modalities for which supervised training data is only sparsely available. In cases where there is a degree of conditional independence between such views, a class of semi-supervised techniques based on maximizing view agreement over unlabeled data has been proven successful in a wide range of machine learning domains. However, these ‘co-training’ or ‘multi-view’ learning methods have had relatively limited application in vision, due in part to the assumption of constant per-channel noise models. In this paper we propose a probabilistic heteroscedastic approach to co-training that simultaneously discovers the amount of noise in a per example basis, while solving the classification task. This results in high performance in the presence of occlusion or other complex observation noise processes. We demonstrate our approach in two domains, multi-view object recognition from low-fidelity sensor networks and audio-visual classification.


BibTeX citation:

@techreport{Christhoudias:EECS-2009-17,
    Author = {Christhoudias, C. Mario and Urtasun, Raquel and Kapoor, Ashish and Darrell, Trevor},
    Title = {Co-training with Noisy Perceptual Observations},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2009},
    Month = {Jan},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-17.html},
    Number = {UCB/EECS-2009-17},
    Abstract = {Many perception and multimedia indexing problems involve datasets that are naturally comprised
of multiple streams or modalities for which supervised training data is only sparsely available. In cases
where there is a degree of conditional independence
between such views, a class of semi-supervised techniques based on maximizing view agreement over unlabeled data has been proven successful in a wide
range of machine learning domains. However, these
‘co-training’ or ‘multi-view’ learning methods have
had relatively limited application in vision, due in
part to the assumption of constant per-channel noise
models. In this paper we propose a probabilistic heteroscedastic approach to co-training that simultaneously discovers the amount of noise in a per example basis, while solving the classification task. This
results in high performance in the presence of occlusion or other complex observation noise processes.
We demonstrate our approach in two domains, multi-view object recognition from low-fidelity sensor networks and audio-visual classification.}
}

EndNote citation:

%0 Report
%A Christhoudias, C. Mario
%A Urtasun, Raquel
%A Kapoor, Ashish
%A Darrell, Trevor
%T Co-training with Noisy Perceptual Observations
%I EECS Department, University of California, Berkeley
%D 2009
%8 January 27
%@ UCB/EECS-2009-17
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-17.html
%F Christhoudias:EECS-2009-17