Leaf image classification with shape context and SIFT descriptors

Zhiyong Wang, Bin Lu, Zheru Chi, Dagan Feng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

23 Citations (Scopus)


Nowadays leaf image classification is very useful for both botanists and ordinary users since advanced imaging devices such as smart phones make it ever easier to capture leaf images for various tasks such as retrieval and classification. Most of existing approaches mainly utilize global shape features. In this paper, we propose to improve leaf image classification by taking both global features and local features into account. As one of the most effective shape features, shape context is utilized as global feature. And SIFT (Scale Invariant Feature Transform) descriptors that have been successfully utilized for object recognition and image classification are selected as local features. Finally, weighted K-NN algorithm is utilized for classification. Experimental results on the large ICL dataset demonstrate that the proposed method outperforms the state-of-the-art.
Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2011
Number of pages5
Publication statusPublished - 1 Dec 2011
Event2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 - Noosa, QLD, Australia
Duration: 6 Dec 20118 Dec 2011


Conference2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011
CityNoosa, QLD


  • image classification
  • K-NN
  • Leaf image
  • shape context
  • SIFT descriptors

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this