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Improved HOG Descriptors in Image Classification with CP Decomposition

  • Tan Vo
  • Dat Tran
  • Wanli Ma
  • Khoa Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.

Keywords

HOG tensor CP decomposition Image Classification Support Vector Machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tan Vo
    • 1
  • Dat Tran
    • 1
  • Wanli Ma
    • 1
    • 2
  • Khoa Nguyen
    • 1
  1. 1.Faculty of Education, Science, Technology & MathematicsUniversity of CanberraAustralia
  2. 2.Department of Computer ScienceUniversity of Houston DowntownUSA

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