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Encoding Spatial Arrangements of Visual Words for Rotation-Invariant Image Classification

  • Hafeez AnwarEmail author
  • Sebastian Zambanini
  • Martin Kampel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

Incorporating the spatial information of visual words enhances the performance of the well-known bag-of-visual words (BoVWs) model for problems like object category recognition. However, object images can undergo various in-plane rotations due to which the spatial information must be added to the BoVWs model in rotation-invariant manner. We present a novel approach to integrate the spatial information to BoVWs model in a rotation-invariant way by encoding the triangular relationship among the positions of identical visual words in the \(2D\) image space. Our proposed BoVWs model is based on densely sampled local features for which the dominant orientations are calculated. Thus we achieve rotation-invariance both globally and locally. We validate our proposed method for rotation-invariance on datasets of ancient coins and butterflies and achieve better performance than the conventional BoVWs model.

Keywords

Spatial Information Visual Word Image Space Vocabulary Construction Visual Vocabulary 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Hafeez Anwar
    • 1
    Email author
  • Sebastian Zambanini
    • 1
  • Martin Kampel
    • 1
  1. 1.Computer Vision LabVienna University of TechnologyViennaAustria

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