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Petal-Image Based Flower Classification via GLCM and RBF-SVM

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Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health (CyberDI 2019, CyberLife 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1138))

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Abstract

Flower identification is a difficult problem in practice. Because there are over 250,000 different kinds of species worldwide so far. Even an experienced flower expert needs reference book to categorize a flower because of the high intra-class variation and inter-class similarity. In this study, an automatic flower recognition method was proposed based on digital image processing and artificial intelligence for petal image. Gray level co-occurrence matrix was employed as the image feature and a support vector machine was trained as the classifier. Three different kernel functions were tested and radial basis function performed best. Experimental results revealed that our approach can achieve state-of-the-art classification performance.

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Authors and Affiliations

  1. School of Education Science, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Zhihai Lu & Siyuan Lu

  2. School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Siyuan Lu

Authors
  1. Zhihai Lu
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  2. Siyuan Lu
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Correspondence to Siyuan Lu .

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Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Huansheng Ning

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Lu, Z., Lu, S. (2019). Petal-Image Based Flower Classification via GLCM and RBF-SVM. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_16

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  • DOI: https://doi.org/10.1007/978-981-15-1925-3_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1924-6

  • Online ISBN: 978-981-15-1925-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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