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Plant Recommender System Based on Multi-label Classification

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

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Abstract

In this paper, a plant recommender system using 2D digital images of leaves is proposed. This system made use of feature fusion technique and the multi-label classification method. Feature fusion technique is used to combine the color, shape, and texture features. Invariant moments, color moments, and Scale Invariant Feature Transform (SIFT) are used to extract the shape, color, and texture features, respectively. The multi-label classification method is capable of classifying samples in more than one class. In multi-label classification method, the nearest neighbor classifier with different metrics is used to match the unknown image with the training images and assigns five different class labels (i.e. recommendations) for each unknown image. The proposed approach was tested using Flavia dataset which consists of 1907 colored images of leaves. The experimental results proved that the accuracy of feature fusion method was much better than all other single features. Moreover, the experiments demonstrated their robustness to provide reliable recommendations.

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Authors and Affiliations

  1. Faculty of Engineering, Ain Shams University, Cairo, Egypt

    Alaa Tharwat & Hani Mahdi

  2. Faculty of Engineering, Suez Canal University, Ismailia, Egypt

    Alaa Tharwat

  3. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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

    Alaa Tharwat & Aboul Ella Hassanien

Authors
  1. Alaa Tharwat
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  2. Hani Mahdi
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  3. Aboul Ella Hassanien
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Corresponding author

Correspondence to Alaa Tharwat .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Tharwat, A., Mahdi, H., Hassanien, A.E. (2017). Plant Recommender System Based on Multi-label Classification. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_79

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_79

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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