Deconvolutional networks
Computer Vision and Pattern Recognition(2010)
摘要
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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关键词
deconvolution,image representation,deconvolutional networks,edge primitives,feature detectors,image representations,images synthesis,pool edge information
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