Evaluation of LBP and HOG Descriptors for Clothing Attribute Description

  • Javier Lorenzo-Navarro
  • Modesto Castrillón
  • Enrique Ramón
  • David Freire
Conference paper

DOI: 10.1007/978-3-319-12811-5_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8811)
Cite this paper as:
Lorenzo-Navarro J., Castrillón M., Ramón E., Freire D. (2014) Evaluation of LBP and HOG Descriptors for Clothing Attribute Description. In: Distante C., Battiato S., Cavallaro A. (eds) Video Analytics for Audience Measurement. VAAM 2014. Lecture Notes in Computer Science, vol 8811. Springer, Cham

Abstract

In this work an experimental study about the capability of the LBP, HOG descriptors and color for clothing attribute classification is presented. Two different variants of the LBP descriptor are considered, the original LBP and the uniform LBP. Two classifiers, Linear SVM and Random Forest, have been included in the comparison because they have been frequently used in clothing attributes classification. The experiments are carried out with a public available dataset, the clothing attribute dataset, that has 26 attributes in total. The obtained accuracies are over 75 % in most cases, reaching 80 % for the necktie or sleeve length attributes.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Javier Lorenzo-Navarro
    • 1
  • Modesto Castrillón
    • 1
  • Enrique Ramón
    • 1
  • David Freire
    • 1
  1. 1.Instituto Universitario SIANI, Campus Universitario de TafiraUniversidad de Las Palmas de Gran CanariaLas PalmasSpain

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