Neural Computing and Applications

, Volume 28, Issue 5, pp 925–939 | Cite as

Evolving weighting schemes for the Bag of Visual Words

  • Hugo Jair Escalante
  • Víctor Ponce-López
  • Sergio Escalera
  • Xavier Baró
  • Alicia Morales-Reyes
  • José Martínez-Carranza
Computational Intelligence for Vision and Robotics

Abstract

The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g., term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.

Keywords

Bag of Visual Words Bag of features Genetic programming Term-weighting schemes Computer vision 

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Hugo Jair Escalante
    • 1
  • Víctor Ponce-López
    • 2
    • 3
    • 4
  • Sergio Escalera
    • 3
    • 4
  • Xavier Baró
    • 2
    • 3
    • 4
  • Alicia Morales-Reyes
    • 1
  • José Martínez-Carranza
    • 1
  1. 1.Instituto Nacional de Astrofísica, Óptica y ElectrónicaPueblaMexico
  2. 2.Universitat Oberta de CatalunyaBarcelonaSpain
  3. 3.University of BarcelonaBarcelonaSpain
  4. 4.Computer Vision CenterBarcelonaSpain

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