Improving HOG with Image Segmentation: Application to Human Detection

  • Yainuvis Socarrás Salas
  • David Vázquez Bermudez
  • Antonio M. López Peña
  • David Gerónimo Gomez
  • Theo Gevers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7517)

Abstract

In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.

We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4.47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.

Keywords

Image Segmentation Object Detection Local Binary Pattern Machine Intelligence IEEE Conf 
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 2012

Authors and Affiliations

  • Yainuvis Socarrás Salas
    • 1
    • 2
  • David Vázquez Bermudez
    • 1
    • 2
  • Antonio M. López Peña
    • 1
    • 2
  • David Gerónimo Gomez
    • 1
    • 2
  • Theo Gevers
    • 1
    • 3
  1. 1.Computer Vision CenterUniversitat Autónoma de BarcelonaBarcelonaSpain
  2. 2.Department of Computer ScienceUniversitat Autónoma de BarcelonaBarcelonaSpain
  3. 3.Informatics Institute, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands

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