Adopting Hybrid Descriptors to Recognise Leaf Images for Automatic Plant Specie Identification

  • Ali A. Al-kharaz
  • Xiaohui Tao
  • Ji Zhang
  • Raid Lafta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)


In recent years, leaf image recognition and classification has become one of the most important subjects in computer vision. Many approaches have been proposed to recognise and classify leaf images relying on features extraction and selection algorithms. In this paper, a concept of distinctive hybrid descriptor is proposed consisting of both global and local features. HSV Colour histogram (HSV-CH) is extracted from leaf images as the global features, whereas Local Binary Pattern after two level wavelet decomposition (WavLBP) is extracted to represent the local characteristics of leaf images. A hybrid method, namely “Hybrid Descriptor” (HD), is then proposed considering both the global and local features. The proposed method has been empirically evaluated using four data sets of leaf images with 256 \(\times \) 256 pixels. Experimental results indicate that the performance of proposed method is promising – the HD outperformed typical leaf image recognising approaches as baseline models in experiments. The presented work makes clear, significant contribution to knowledge advancement in leaf recognition and image classification.


Leaf image Local feature Global feature Colour histogram Texture LBP Wavelet 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ali A. Al-kharaz
    • 1
    • 2
  • Xiaohui Tao
    • 1
  • Ji Zhang
    • 1
  • Raid Lafta
    • 1
  1. 1.School of Agricultural, Computational and Environmental SciencesUniversity of Southern QueenslandToowoombaAustralia
  2. 2.Department of IT, Technical College of ManagementBaghdad Foundation of Technical EducationBaghdadIraq

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