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Dogs Animal Recognition System in IoT Environment Based on Orthogonal Statistical Adapted Local Binary Pattern

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))

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Abstract

The Internet of Things (IoT) is gaining more importance in our modern life because of its wide range of applications. In this paper, we propose a novel dog recognition system for recognizing dogs from camera images. The proposed technique is based on a new definition to the classical Local Binary Pattern (LBP). In this definition, the value of each pixel changes according to its weight in a predetermined block size of an image. The new central pixel value thresholds its neighborhood is creating a new pixel value for the central pixel. To reduce the dimension of the new definition histogram we consider only the orthogonal pixels in building the new histogram. Finally, the classification accuracy is computed using the nearest neighbor classifier with Chi-square as a dissimilarity measure. Experiments conducted on a data set of 17 different classes of dogs; show that the proposed system performs better than traditional methods (single scale) LBP and Adaptive Local Binary Pattern (ALBP), Statistical Adaptive Local Binary Pattern (SALBP) regarding accuracy.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Menoufia University, Shebin El-Koom, Egypt

    Abdallah A. Mohamed, Hani M. Ibrahem & Walid A. Dabour

  2. Department of Information Technology, Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  3. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Abdallah A. Mohamed, Hani M. Ibrahem, Walid A. Dabour & Aboul Ella Hassanien

Authors
  1. Abdallah A. Mohamed
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  2. Hani M. Ibrahem
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  3. Walid A. Dabour
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  4. Aboul Ella Hassanien
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Corresponding author

Correspondence to Abdallah A. Mohamed .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Mohamed, A.A., Ibrahem, H.M., Dabour, W.A., Hassanien, A.E. (2018). Dogs Animal Recognition System in IoT Environment Based on Orthogonal Statistical Adapted Local Binary Pattern. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_72

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  • DOI: https://doi.org/10.1007/978-3-319-64861-3_72

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

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