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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Integration of active learning in a collaborative CRF
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Oscar Martinez, Dept. of Electrical and Computer Engineering, University of Miami, 1251 Memorial Dr, Coral Gables, FL-33146 USA
Gabriel Tsechpenakis, Dept. of Electrical and Computer Engineering, University of Miami, 1251 Memorial Dr, Coral Gables, FL-33146 USA
We present an active learning approach for visual multiple object class recognition, using a Conditional Random Field (CRF) formulation. We name our graphical model ‘collaborative’, because it infers class posteriors in in stances of occlusion and missing information by assessing the joint appearance and geometric assortment of neighboring sites. The model can handle scenes containing multiple classes and multiple objects inherently while using the confidence of its predictions to enforce label uniformity in areas where evidence supports similarity. Our method uses classification uncertainty to dynamically select new train ing samples to retrain the discriminative classifiers used in the CRF. We demonstrate the performance of our approach using cluttered scenes containing multiple objects and multiple class instances.
Citation:
Oscar Martinez, Gabriel Tsechpenakis, "Integration of active learning in a collaborative CRF," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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