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 |
Keywords
- Deep convolutional neural network
- Plant classification
- Subset feature learning
Cite this
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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
TY - GEN
T1 - Content specific feature learning for fine-grained plant classification
AU - Ge, ZongYuan
AU - McCool, Chris
AU - Sanderson, Conrad
AU - Corke, Peter
PY - 2015/1/1
Y1 - 2015/1/1
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
UR - http://www.scopus.com/inward/record.url?scp=84982843399&partnerID=8YFLogxK
M3 - Conference Paper
VL - 1391
T3 - CEUR Workshop Proceedings
BT - CLEF 2015
A2 - Cappellato, Linda
A2 - Ferro, Nicola
A2 - Jones, Gareth J. F.
A2 - San Juan, Eric
PB - RWTH Aachen University
CY - Aachen Germany
ER -