Content specific feature learning for fine-grained plant classification

ZongYuan Ge, Chris McCool, Conrad Sanderson, Peter Corke

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

Abstract

We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2015 plant task. Our system learns a content speciffic feature for various plant parts such as branch, leaf, fruit, ower and stem. These features are learned using a deep convolutional neural network. Experiments on the LifeCLEF 2015 plant dataset show that the proposed method achieves good performance with a score of 0:633 on the test set.

Original languageEnglish
Title of host publicationCLEF 2015
Subtitle of host publicationCLEF 2015 Working Notes
EditorsLinda Cappellato, Nicola Ferro, Gareth J. F. Jones, Eric San Juan
Place of PublicationAachen Germany
PublisherRWTH Aachen University
Number of pages7
Volume1391
Publication statusPublished - 1 Jan 2015
Externally publishedYes
EventConference and Labs of the Evaluation Forum 2015: Experimental IR meets Multilinguality, Multimodality, and Interaction - University of Toulouse, Toulouse, France
Duration: 8 Sep 201111 Sep 2015
Conference number: 6th
http://clef2015.clef-initiative.eu/

Publication series

NameCEUR Workshop Proceedings
PublisherRheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V
ISSN (Print)1613-0073

Conference

ConferenceConference and Labs of the Evaluation Forum 2015
Abbreviated titleCLEF 2015
CountryFrance
CityToulouse
Period8/09/1111/09/15
Internet address

Keywords

  • Deep convolutional neural network
  • Plant classification
  • Subset feature learning

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