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      Robust object recognition with cortex-like mechanisms.

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          Abstract

          We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex.

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          Author and article information

          Journal
          IEEE Trans Pattern Anal Mach Intell
          IEEE transactions on pattern analysis and machine intelligence
          Institute of Electrical and Electronics Engineers (IEEE)
          0162-8828
          0098-5589
          Mar 2007
          : 29
          : 3
          Affiliations
          [1 ] Massachusetts Institute of Technology, Center for Biological and Computational Learning, McGovern Institute for Brain Research and Brain & Cognitive Sciences Department, MA 02139, USA. serre@mit.edu
          Article
          10.1109/TPAMI.2007.56
          17224612
          8b813645-002c-476d-814f-88cbe2892b3b

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