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

Cite this

Ge, Z., McCool, C., Sanderson, C., & Corke, P. (2015). Content specific feature learning for fine-grained plant classification. In L. Cappellato, N. Ferro, G. J. F. Jones, & E. San Juan (Eds.), CLEF 2015: CLEF 2015 Working Notes (Vol. 1391). (CEUR Workshop Proceedings). Aachen Germany: RWTH Aachen University.
Ge, ZongYuan ; McCool, Chris ; Sanderson, Conrad ; Corke, Peter. / Content specific feature learning for fine-grained plant classification. CLEF 2015: CLEF 2015 Working Notes. editor / Linda Cappellato ; Nicola Ferro ; Gareth J. F. Jones ; Eric San Juan. Vol. 1391 Aachen Germany : RWTH Aachen University, 2015. (CEUR Workshop Proceedings).
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title = "Content specific feature learning for fine-grained plant classification",
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.",
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Ge, Z, McCool, C, Sanderson, C & Corke, P 2015, Content specific feature learning for fine-grained plant classification. in L Cappellato, N Ferro, GJF Jones & E San Juan (eds), CLEF 2015: CLEF 2015 Working Notes. vol. 1391, CEUR Workshop Proceedings, RWTH Aachen University, Aachen Germany, Conference and Labs of the Evaluation Forum 2015, Toulouse, France, 8/09/11.

Content specific feature learning for fine-grained plant classification. / Ge, ZongYuan; McCool, Chris; Sanderson, Conrad; Corke, Peter.

CLEF 2015: CLEF 2015 Working Notes. ed. / Linda Cappellato; Nicola Ferro; Gareth J. F. Jones; Eric San Juan. Vol. 1391 Aachen Germany : RWTH Aachen University, 2015. (CEUR Workshop Proceedings).

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

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AU - Sanderson, Conrad

AU - Corke, Peter

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N2 - 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.

AB - 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.

KW - Deep convolutional neural network

KW - Plant classification

KW - Subset feature learning

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M3 - Conference Paper

VL - 1391

T3 - CEUR Workshop Proceedings

BT - CLEF 2015

A2 - Cappellato, Linda

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A2 - Jones, Gareth J. F.

A2 - San Juan, Eric

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Ge Z, McCool C, Sanderson C, Corke P. Content specific feature learning for fine-grained plant classification. In Cappellato L, Ferro N, Jones GJF, San Juan E, editors, CLEF 2015: CLEF 2015 Working Notes. Vol. 1391. Aachen Germany: RWTH Aachen University. 2015. (CEUR Workshop Proceedings).