An Efficient Multi-scale Overlapped Block LBP Approach for Leaf Image Recognition

  • Xiao-Ming Ren
  • Xiao-Feng Wang
  • Yang Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)


In this paper, an effective method based on multi-scale overlapped block LBP is proposed for plant leaf image recognition. Firstly, multi-scale pyramid is employed in order to improve the leaf data utilization. For each scale, each training image is divided into several equal overlapping blocks to extract the LBP histograms. Then, the PCA method is used for LBP feature dimension reduction. Finally, the recognition experiments are performed by using the SVM classifier. We compare the proposed method with Histogram of Oriented Gradients (HOG) method and Inner-Distance Shape Context (IDSC) method on Swedish leaf dataset and our ICL leaf dataset. The experimental results show that the proposed method achieves better performance than IDSC and HOG.


Leaf recognition Local Binary Pattern Multi-Scale pyramid Principal Component Analysis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiao-Ming Ren
    • 1
    • 2
  • Xiao-Feng Wang
    • 2
    • 3
  • Yang Zhao
    • 1
    • 2
  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Intelligent Computing LaboratoryHefei Institute of Intelligent Machines, Chinese Academy of SciencesHefeiChina
  3. 3.Key Lab of Network and Intelligent Information ProcessingHefei UniversityHefeiChina

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