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2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)
San Francisco, CA, USA
June 13, 2010 to June 18, 2010
ISBN: 978-1-4244-6984-0
pp: 2528-2535
Matthew D. Zeiler , Dept. of Computer Science, Courant Institute, New York University
Dilip Krishnan , Dept. of Computer Science, Courant Institute, New York University
Graham W. Taylor , Dept. of Computer Science, Courant Institute, New York University
Rob Fergus , Dept. of Computer Science, Courant Institute, New York University
ABSTRACT
Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning framework where features that capture these mid-level cues spontaneously emerge from image data. Our approach is based on the convolutional decomposition of images under a spar-sity constraint and is totally unsupervised. By building a hierarchy of such decompositions we can learn rich feature sets that are a robust image representation for both the analysis and synthesis of images.
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CITATION

R. Fergus, M. D. Zeiler, G. W. Taylor and D. Krishnan, "Deconvolutional networks," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), San Francisco, CA, USA, 2010, pp. 2528-2535.
doi:10.1109/CVPR.2010.5539957
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