Jonathan Barron and Jitendra Malik

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2010-94

June 12, 2010

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-94.pdf

We introduce a model for joint texture classification and segmentation that learns not only *how* to classify accurately, but *when* to classify efficiently. This model, combined with a complementary efficient feature representation that we describe, allows us to move beyond naive sliding-window classification strategies into sub-linear coarse-to-fine classification of an entire image. Recognition is formulated as a scale-space traversal through the image in which we can ``stop short'' at coarse scales, dramatically increasing both the speed and the accuracy of classification. Unlike other models, ours is constructed such that the classification produced when stopping-short is exact (that is, equivalent to the classification produced when not stopping-short), because coarse-to-fine efficiency is directly incorporated into the model. Classification is demonstrated on partially- and fully-annotated datasets of satellite and medical imagery.

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BibTeX citation:

@techreport{Barron:EECS-2010-94,
    Author= {Barron, Jonathan and Malik, Jitendra},
    Title= {Discovering Efficiency in Coarse-To-Fine Texture Classification},
    Year= {2010},
    Month= {Jun},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-94.html},
    Number= {UCB/EECS-2010-94},
    Abstract= {We introduce a model for joint texture classification and segmentation that learns not only *how* to classify accurately, but *when* to classify efficiently. This model, combined with a complementary efficient feature representation that we describe, allows us to move beyond naive sliding-window classification strategies into sub-linear coarse-to-fine classification of an entire image. Recognition is formulated as a scale-space traversal through the image in which we can ``stop short'' at coarse scales, dramatically increasing both the speed and the accuracy of classification. Unlike other models, ours is constructed such that the classification produced when stopping-short is exact (that is, equivalent to the classification produced when not stopping-short), because coarse-to-fine efficiency is directly incorporated into the model. Classification is demonstrated on partially- and fully-annotated datasets of satellite and medical imagery.},
}

EndNote citation:

%0 Report
%A Barron, Jonathan 
%A Malik, Jitendra 
%T Discovering Efficiency in Coarse-To-Fine Texture Classification
%I EECS Department, University of California, Berkeley
%D 2010
%8 June 12
%@ UCB/EECS-2010-94
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-94.html
%F Barron:EECS-2010-94