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Transfer Learning of Mixture Texture Models

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Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12496))

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Abstract

A transfer learning approach for multidimensional parametric mixture random field-based textural representation is introduced. The proposed transfer learning approach allows alleviating the multidimensional mixture models requirement for sufficiently large, but not always available, learning data sets. These compound random field models consist of an underlying structure model that controls transitions between several sub-models, each of them has different characteristics. The structure model proposed is a two-dimensional probabilistic mixture model, either of the Bernoulli or Gaussian mixture type. Local textures are modeled using the fully multispectral three-dimensional Gaussian mixture sub-models. Both presented compound random field models allow the reproduction of, compresses, edits, and enlarges a given measured color, multispectral, or bidirectional texture function (BTF) texture so that ideally, both measured and synthetic textures are visually indiscernible.

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Acknowledgements

The Czech Science Foundation project GAČR 19-12340S supported this research.

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Authors and Affiliations

  1. The Institute of Information Theory and Automation of the Czech Academy of Sciences, Prague, Czechia

    Michal Haindl & Vojtěch Havlíček

  2. Faculty of Management, University of Economics, Jindřichův Hradec, Czechia

    Michal Haindl

Authors
  1. Michal Haindl
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  2. Vojtěch Havlíček
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Corresponding author

Correspondence to Michal Haindl .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Thua Thien Hue Center of Information Technology, Hue, Vietnam

    Bao Hung Hoang

  3. Vietnam - Korea University of Information and Communication Technology, University of Da Nang, Da Nang, Vietnam

    Cong Phap Huynh

  4. Department of Computer Engineering, Yeungnam University, Gyeungsan, Korea (Republic of)

    Dosam Hwang

  5. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

  6. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Haindl, M., Havlíček, V. (2020). Transfer Learning of Mixture Texture Models. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_65

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  • DOI: https://doi.org/10.1007/978-3-030-63007-2_65

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  • Print ISBN: 978-3-030-63006-5

  • Online ISBN: 978-3-030-63007-2

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