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Circular Local Directional Pattern for Texture Analysis

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Mining Intelligence and Knowledge Exploration (MIKE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11987))

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Abstract

This paper presents a novel texture feature extraction method, Circular Local Directional Pattern (CILDP), that is inspired by Local Binary pattern (LBP) and Local Directional Pattern (LDP). This method relies on circular shape to compute the directional edge responses based on Kirsch Masks using different radiuses. The performance of the proposed method is evaluated using five classifiers on textures from the Kylberg dataset. Results achieved establish that the proposed method consistently outperforms LBP and LDP when different radiuses are considered.

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Authors and Affiliations

  1. University of KwaZulu-Natal, Durban, 4041, South Africa

    Abuobayda M. M. Shabat & Jules R. Tapamo

Authors
  1. Abuobayda M. M. Shabat
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  2. Jules R. Tapamo
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Corresponding author

Correspondence to Jules R. Tapamo .

Editor information

Editors and Affiliations

  1. National Institute of Technology, Goa, India

    Purushothama B. R.

  2. National Institute of Technology, Goa, India

    Veena Thenkanidiyoor

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

  4. Indian Institute of Information Technology, Sri City, India

    Odelu Vanga

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Shabat, A.M.M., Tapamo, J.R. (2020). Circular Local Directional Pattern for Texture Analysis. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_13

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  • DOI: https://doi.org/10.1007/978-3-030-66187-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66186-1

  • Online ISBN: 978-3-030-66187-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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