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A Genetic Programming Method for Scale-Invariant Texture Classification

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

Abstract

Texture offers an effective characterization of image shape and orientation. Thus, a predominant task is to detect and extract texture features that discriminate accurately images within different semantic classes. The challenge resides in making these features invariant to several changes, such as affine transformation and viewpoint change, in order to ensure their robustness. Besides, the training phase requires a large number of images. To deal with these issues, Genetic Programming (GP) is adopted in this work with the intention of classifying precisely texture images using some training images per class. In fact, in order to automatically generate a descriptor that is invariant to illumination, rotation and scale; the proposed method combines GP with the scale extraction technique involved by SIFT. The performance of the proposed method is validated on five challenging datasets of non-scaled as well as scaled texture images. Results show that the method is robust not only to scale but also to rotation, while achieving significant performance compared to the state of the art methods.

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Authors and Affiliations

  1. Institut Supérieur d’Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), Université de Tunis El Manar, Ariana, Tunisia

    Haythem Ghazouani, Walid Barhoumi & Yosra Antit

  2. Ecole Nationale d’Ingénieurs de Carthage, Université de Carthage, Tunis-Carthage, Tunisia

    Haythem Ghazouani & Walid Barhoumi

Authors
  1. Haythem Ghazouani
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  2. Walid Barhoumi
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  3. Yosra Antit
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Corresponding author

Correspondence to Walid Barhoumi .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Ghazouani, H., Barhoumi, W., Antit, Y. (2020). A Genetic Programming Method for Scale-Invariant Texture Classification. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_47

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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