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A Universal Visual Dictionary Learned from Natural Scenes for Recognition

  • Li Ding
  • Jinhua Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

Inspired by the efficient coding hypothesis and simple-to-complex cell hierarchy of the visual system, we study a universal visual dictionary learned from natural scenes using sparse coding for recognition. The vocabularies are similar to V1 simple cells receptive fields. Max pooling is done in a local region (”block”) so that the features are translation invariant, which is the function of complex cells. Macro-features of a grid of overlapping spatial blocks are built and fed to a linear SVM classifier for recognition. We have tested the learned universal visual dictionary on different recognition tasks and demonstrated the effectiveness of the model.

Keywords

sparse coding dictionary learning object recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Li Ding
    • 1
  • Jinhua Xu
    • 1
  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

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