Multimedia Tools and Applications

, Volume 76, Issue 3, pp 4599–4615 | Cite as

Plant identification via multipath sparse coding

  • Heyan Zhu
  • Xinyuan Huang
  • Shengping Zhang
  • Pong C. Yuen
Article
  • 173 Downloads

Abstract

In this paper, we propose a novel plant identification method based on multipath sparse coding using SIFT features, which avoids the need of feature engineering and the reliance on botanical taxonomy. In particular, the proposed method uses five paths to model the shape and texture features of plant images, and at each path it learns the dictionaries with different sizes using hierarchical sparse coding. Finally, we apply the learned representation for plant identification using linear SVM for classification. We evaluate the proposed method on several plant datasets and find that multi-organ is more informative than single organ for botanist. Experimental results also validate that the proposed method outperforms the state-of-the-art methods.

Keywords

Plant identification Multipath sparse coding SIFT descriptor Multi-organ Linear SVM 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Heyan Zhu
    • 1
    • 2
  • Xinyuan Huang
    • 3
  • Shengping Zhang
    • 4
  • Pong C. Yuen
    • 5
  1. 1.School of InformationBeijing Forestry UniversityBeijingChina
  2. 2.School of Opto-Electronic InformationYantai UniversityYantaiChina
  3. 3.Institute of Animation and Digital ArtCommunication University of ChinaBeijingChina
  4. 4.School of Computer Science and TechnologyHarbin Institute of TechnologyWeihaiChina
  5. 5.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina

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