Content specific feature learning for fine-grained plant classification

ZongYuan Ge; Chris McCool; Conrad Sanderson; Peter Corke

Content specific feature learning for fine-grained plant classification

ZongYuan Ge, Chris McCool, Conrad Sanderson, Peter Corke

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-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 language	English
Title of host publication	CLEF 2015
Subtitle of host publication	CLEF 2015 Working Notes
Editors	Linda Cappellato, Nicola Ferro, Gareth J. F. Jones, Eric San Juan
Place of Publication	Aachen Germany
Publisher	RWTH Aachen University
Number of pages	7
Volume	1391
Publication status	Published - 1 Jan 2015
Externally published	Yes
Event	Conference and Labs of the Evaluation Forum 2015: Experimental IR meets Multilinguality, Multimodality, and Interaction - University of Toulouse, Toulouse, France Duration: 8 Sep 2011 → 11 Sep 2015 Conference number: 6th http://clef2015.clef-initiative.eu/

Publication series

Name	CEUR Workshop Proceedings
Publisher	Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V
ISSN (Print)	1613-0073

Conference

Conference	Conference and Labs of the Evaluation Forum 2015
Abbreviated title	CLEF 2015
Country	France
City	Toulouse
Period	8/09/11 → 11/09/15
Internet address	http://clef2015.clef-initiative.eu/

Keywords

Deep convolutional neural network
Plant classification
Subset feature learning

Access to Document

251378017-oaFinal published version, 554 KB

Cite this

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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.",

keywords = "Deep convolutional neural network, Plant classification, Subset feature learning",

author = "ZongYuan Ge and Chris McCool and Conrad Sanderson and Peter Corke",

year = "2015",

month = jan,

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language = "English",

volume = "1391",

series = "CEUR Workshop Proceedings",

publisher = "RWTH Aachen University",

editor = "Linda Cappellato and Nicola Ferro and Jones, {Gareth J. F. } and {San Juan}, Eric",

booktitle = "CLEF 2015",

note = "Conference and Labs of the Evaluation Forum 2015 : Experimental IR meets Multilinguality, Multimodality, and Interaction, CLEF 2015 ; Conference date: 08-09-2011 Through 11-09-2015",

url = "http://clef2015.clef-initiative.eu/",

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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. <http://ceur-ws.org/Vol-1391/39-CR.pdf>

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 proceeding › Conference Paper › Research › peer-review

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AU - Sanderson, Conrad

AU - Corke, Peter

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