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Texture-Based Image Transformations for Improved Deep Learning Classification

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12702))

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Abstract

In this paper, we examine the effect of texture-based image transformation on classification performance. A novel combination of mathematical morphology operations and contrast-limited adaptive histogram equalization is proposed to enhance image textural features. The suggested operations are applied in HSV colour space, where the intensity component is separated from the colour information. Two publicly available, texture-oriented datasets are used for evaluation in this study. The KTH-TIPS2-b dataset is utilised to illustrate the general effectiveness and applicability of the proposed solution on standardized texture images. The Virus Texture dataset is subsequently used to demonstrate a statistically significant classification improvement in a particular biomedical image recognition task.

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Acknowledgement

The work was supported from European Regional Development Fund-Project “Postdoc2@MUNI” (No. CZ.02.2.69/0.0/0.0/18\(\_\)053/0016952), and from Ministry of Education, Science, and Techn. Development of the Republic of Serbia project ON174008, and through the Project EFFICACY from the University of Southern Denmark.

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Authors and Affiliations

  1. Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czech Republic

    Tomáš Majtner

  2. Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

    Buda Bajić

  3. The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark

    Jürgen Herp

Authors
  1. Tomáš Majtner
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  2. Buda Bajić
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  3. Jürgen Herp
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Corresponding author

Correspondence to Tomáš Majtner .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  2. Universidade Estadual Paulista, São Paulo, Brazil

    João Paulo Papa

  3. University of the Balearic Islands, Palma de Mallorca, Spain

    Manuel González Hidalgo

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Majtner, T., Bajić, B., Herp, J. (2021). Texture-Based Image Transformations for Improved Deep Learning Classification. In: Tavares, J.M.R.S., Papa, J.P., González Hidalgo, M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2021. Lecture Notes in Computer Science(), vol 12702. Springer, Cham. https://doi.org/10.1007/978-3-030-93420-0_20

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  • DOI: https://doi.org/10.1007/978-3-030-93420-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93419-4

  • Online ISBN: 978-3-030-93420-0

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