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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Scopus citations


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.
Number of pages5
ISBN (Electronic)9780989319317
StatePublished - 7 Oct 2014
Event2014 Science and Information Conference, SAI 2014 - London, United Kingdom
Duration: 27 Aug 201429 Aug 2014

Publication series

NameProceedings of 2014 Science and Information Conference, SAI 2014


Conference2014 Science and Information Conference, SAI 2014
Country/TerritoryUnited Kingdom


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


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