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Evolving weighting schemes for the Bag of Visual Words

  • Computational Intelligence for Vision and Robotics
  • Published:
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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.

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Notes

  1. One should note the text mining community has proposed variants that aim to soften such assumptions, e.g., using n-grams [2], still the BoW is very competitive with such formulations.

  2. Please note that traditional weighting schemes have been proposed by researchers based on their own experiences and biases, making strong assumptions and relying on intuition.

  3. Please note that in GP, for each individual, either mutation or crossover is performed each time, but not both. This is different from other variants like genetic algorithms.

  4. Matlab files with the predefined partitions are publicly available under request.

  5. PHOW is an extension to the raw BoVW formulation that aims at incorporating spatial information by means of a pyramidal structure, see [3] for details.

  6. Please note that estimating the fitness function is quite efficient, as it is based on a fast approximation to a linear SVM. So this method can be used for most computer vision applications. Also, we emphasize that the fitness function is only estimated during the learning process, which has to be done a single time and most of the times is performed offline.

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Acknowledgments

This work was supported by CONACyT under Project Grant No. CB-2014-241306 (Clasificación y recuperación de imágenes mediante técnicas de minería de textos) and Spanish Ministry of Economy and Competitiveness TIN2013-43478-P. Víctor Ponce-López is supported by Fellowship No. 2013FI-B01037 and Project TIN2012-38187-C03-02.

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Authors and Affiliations

  1. Instituto Nacional de Astrofísica, Óptica y Electrónica, 72840, Puebla, Mexico

    Hugo Jair Escalante, Alicia Morales-Reyes & José Martínez-Carranza

  2. Universitat Oberta de Catalunya, Barcelona, Spain

    Víctor Ponce-López & Xavier Baró

  3. University of Barcelona, Barcelona, Spain

    Víctor Ponce-López, Sergio Escalera & Xavier Baró

  4. Computer Vision Center, Barcelona, Spain

    Víctor Ponce-López, Sergio Escalera & Xavier Baró

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  1. Hugo Jair Escalante
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  2. Víctor Ponce-López
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  3. Sergio Escalera
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  4. Xavier Baró
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  5. Alicia Morales-Reyes
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  6. José Martínez-Carranza
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Correspondence to Hugo Jair Escalante.

Additional information

This paper is an extended and improved version of [12] and it is being submitted to the Special Issue on Computational Intelligence for Vision and Robotics of the Neural Computing and Applications Journal.

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Escalante, H.J., Ponce-López, V., Escalera, S. et al. Evolving weighting schemes for the Bag of Visual Words. Neural Comput & Applic 28, 925–939 (2017). https://doi.org/10.1007/s00521-016-2223-x

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