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The Complete Study of the Movement Strategies of Trained Agents for Visual Descriptors of Advertising Signs

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13644))

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Abstract

We provide a complete description of our investigation into specialized visual descriptors application as a part of combined classifier architecture for advertising signboards photographs classification problem. We propose novel types of descriptors (pure convolutional neural networks based and based on trainable parametrized agent movement strategies) showing the state of the art results in the extraction of visual characteristics and related semantics of text fonts presented on a sign. To provide comparisons of developed approaches and its effectiveness examination we used two datasets of commercial building facade photographs grouped by the type of presented business.

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Notes

  1. 1.

    https://github.com/madrugado/signboard-classification-dataset.

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Authors and Affiliations

  1. ITMO University, St. Petersburg, 197101, Russia

    Aleksei Samarin, Alexander Savelev, Aleksei Toropov, Alina Dzestelova, Valentin Malykh & Elena Mikhailova

  2. St. Petersburg Electrotechnical University “LETI”, St. Petersburg, 197022, Russia

    Alexandr Motyko

Authors
  1. Aleksei Samarin
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  2. Alexander Savelev
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  3. Aleksei Toropov
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  4. Alina Dzestelova
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  5. Valentin Malykh
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  6. Elena Mikhailova
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  7. Alexandr Motyko
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Corresponding author

Correspondence to Alexandr Motyko .

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Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. Ontario Tech University, Oshawa, ON, Canada

    Bill Kapralos

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Samarin, A. et al. (2023). The Complete Study of the Movement Strategies of Trained Agents for Visual Descriptors of Advertising Signs. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_45

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  • DOI: https://doi.org/10.1007/978-3-031-37742-6_45

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  • Online ISBN: 978-3-031-37742-6

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