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Assessing the Distinctiveness and Representativeness of Visual Vocabularies

  • Leonardo ChangEmail author
  • Airel Pérez-Suárez
  • Máximo Rodríguez-Collada
  • José Hernández-Palancar
  • Miguel Arias-Estrada
  • Luis Enrique Sucar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

Bag of Visual Words is one of the most widely used approaches for representing images for object categorization; however, it has several drawbacks. In this paper, we propose three properties and their corresponding quantitative evaluation measures to assess the ability of a visual word to represent and discriminate an object class. Additionally, we also introduce two methods for ranking and filtering visual vocabularies and a soft weighting method for BoW image representation. Experiments conducted on the Caltech-101 dataset showed the improvement introduced by our proposals, which obtained the best classification results for the highest compression rates when compared with a state-of-the-art mutual information based method for feature selection.

Keywords

Bag of visual words Visual vocabulary Object categorization Object recognition 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leonardo Chang
    • 1
    • 2
    Email author
  • Airel Pérez-Suárez
    • 1
  • Máximo Rodríguez-Collada
    • 1
  • José Hernández-Palancar
    • 1
  • Miguel Arias-Estrada
    • 2
  • Luis Enrique Sucar
    • 2
  1. 1.Advanced Technologies Application Center (CENATAV)HavanaCuba
  2. 2.Instituto Nacional de AstrofísicaÓptica y Electrónica (INAOE)PueblaMexico

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