Comparative study of leaf image recognition with a novel learning-based approach

Jou Ken Hsiao, Li Wei Kang*, Ching Long Chang, Chih Yang Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)

Abstract

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

Original languageEnglish
Title of host publicationProceedings of 2014 Science and Information Conference, SAI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages389-393
Number of pages5
ISBN (Electronic)9780989319317
DOIs
Publication statusPublished - 2014 Oct 7
Externally publishedYes
Event2014 Science and Information Conference, SAI 2014 - London, United Kingdom
Duration: 2014 Aug 272014 Aug 29

Publication series

NameProceedings of 2014 Science and Information Conference, SAI 2014

Other

Other2014 Science and Information Conference, SAI 2014
Country/TerritoryUnited Kingdom
CityLondon
Period2014/08/272014/08/29

Keywords

  • bag-of-words
  • classification
  • dictionary learning
  • leaf recognition
  • plant identification

ASJC Scopus subject areas

  • Information Systems

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