Plant identification via multipath sparse coding

Heyan Zhu, Xinyuan Huang*, Shengping Zhang, Pong Chi YUEN

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4599-4615
Number of pages17
JournalMultimedia Tools and Applications
Volume76
Issue number3
DOIs
Publication statusPublished - 1 Feb 2017

Scopus Subject Areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

User-Defined Keywords

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

Fingerprint

Dive into the research topics of 'Plant identification via multipath sparse coding'. Together they form a unique fingerprint.

Cite this