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Neural Object Recognition by Hierarchical Learning and Extraction of Essential Shapes

  • Daniel Oberhoff
  • Marina Kolesnik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)

Abstract

We present a hierarchical system for object recognition that models neural mechanisms of visual processing identified in the mammalian ventral stream. The system is composed of neural units organized in a hierarchy of layers with increasing complexity. A key feature of the system is that the neural units learn their preferred patterns from visual input alone. Through this “soft wiring” of neural units the system becomes tuned for target object classes through pure visual experience and with no prior labeling. Object labels are only introduced to train a classifier on the system’s output. The system’s tuning takes place in a feed-forward path. We also present a neural mechanism for back projection of the learned image patterns down the hierarchical layers. This feedback mechanism could serve as a starting point for integration of what- and where-information processed by the ventral and dorsal stream. We test the neural system with natural images from publicly available datasets of natural scenes and handwritten digits.

Keywords

Object Recognition Recognition Rate Dorsal Stream Ventral Stream Handwritten Digit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Oberhoff
    • 1
  • Marina Kolesnik
    • 1
  1. 1.Fraunhofer Institut FIT, Schloss Birlinghoven, 53754 Sankt Augustin 

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