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Fuzzy Preprocessing for Semi-supervised Image Classification in Modern Industry

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

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Abstract

We are focusing on image classification in industrial processing taking into account the most problematic issue of the processing: the lack of labeled data. Here, we are considering three datasets: the first one is an unsorted collection of all types of manufactured products and includes 100 images per class. The second one consists of products sorted into particular classes by a specialized employee and includes only ten images per class. The last one includes a massive volume of labeled images, but it is used only for the proposal validation. As the configuration is challenging for neural networks, we propose to use Image Represented by a Fuzzy Function in order to enrich original image information. We solve the task using various autoencoder architectures and prove that such the proposal increases the autoencoders success rate.

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Acknowledgment

The work was supported from ERDF/ESF “Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region” (No. CZ.02.1.01/0.0/0.0/17_049/0008414).

For more supplementary materials and overview of our lab work see http://graphicwg.irafm.osu.cz/storage/pr/links.html.

Author information

Authors and Affiliations

  1. Institute for Research and Applications of Fuzzy Modeling, Centre of Excellence IT4Innovations, University of Ostrava, Ostrava, Czech Republic

    Petr Hurtik & Vojtěch Molek

Authors
  1. Petr Hurtik
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  2. Vojtěch Molek
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Corresponding author

Correspondence to Petr Hurtik .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Hurtik, P., Molek, V. (2019). Fuzzy Preprocessing for Semi-supervised Image Classification in Modern Industry. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_1

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20517-1

  • Online ISBN: 978-3-030-20518-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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