Abstract
We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2016 plant task. Our system learns two deep convolutional neural network models. The first is a domain-specific model and the second is a mixture of content specific models, one for each of the plant organs such as branch, leaf, fruit, ower and stem. We combine these two models and experiments on the PlantCLEF2016 dataset show that this approach provides an improvement over the baseline system with the mean average precision improving from 0:603 to 0:629 on the test set.
| Original language | English |
|---|---|
| Title of host publication | CLEF 2016 |
| Subtitle of host publication | CLEF2016 Working Notes |
| Editors | Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonald |
| Place of Publication | Aachen Germany |
| Publisher | RWTH Aachen University |
| Pages | 511-517 |
| Number of pages | 7 |
| Volume | 1609 |
| Publication status | Published - 1 Jan 2016 |
| Externally published | Yes |
| Event | Conference and Labs of the Evaluation Forum 2016: Information Access Evaluation meets Multilinguality, Multimodality and Interaction - University of Évora, Evora, Portugal Duration: 5 Sep 2016 → 8 Sep 2016 Conference number: 7th http://clef2016.clef-initiative.eu/index.php |
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 2016 |
|---|---|
| Abbreviated title | CLEF 2016 |
| Country | Portugal |
| City | Evora |
| Period | 5/09/16 → 8/09/16 |
| Internet address |
Keywords
- Deep Convolutional Neural Network
- Mixture Of Deep Convolutional Neural Networks
- Plant Classification
Cite this
}
Feature learning via mixtures of DCNNs for fine-grained plant classification. / McCool, Chris; Ge, ZongYuan; Corke, Peter.
CLEF 2016: CLEF2016 Working Notes. ed. / Krisztian Balog; Linda Cappellato; Nicola Ferro; Craig Macdonald. Vol. 1609 Aachen Germany : RWTH Aachen University, 2016. p. 511-517 (CEUR Workshop Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Feature learning via mixtures of DCNNs for fine-grained plant classification
AU - McCool, Chris
AU - Ge, ZongYuan
AU - Corke, Peter
PY - 2016/1/1
Y1 - 2016/1/1
N2 - We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2016 plant task. Our system learns two deep convolutional neural network models. The first is a domain-specific model and the second is a mixture of content specific models, one for each of the plant organs such as branch, leaf, fruit, ower and stem. We combine these two models and experiments on the PlantCLEF2016 dataset show that this approach provides an improvement over the baseline system with the mean average precision improving from 0:603 to 0:629 on the test set.
AB - We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2016 plant task. Our system learns two deep convolutional neural network models. The first is a domain-specific model and the second is a mixture of content specific models, one for each of the plant organs such as branch, leaf, fruit, ower and stem. We combine these two models and experiments on the PlantCLEF2016 dataset show that this approach provides an improvement over the baseline system with the mean average precision improving from 0:603 to 0:629 on the test set.
KW - Deep Convolutional Neural Network
KW - Mixture Of Deep Convolutional Neural Networks
KW - Plant Classification
UR - http://www.scopus.com/inward/record.url?scp=85019582545&partnerID=8YFLogxK
M3 - Conference Paper
VL - 1609
T3 - CEUR Workshop Proceedings
SP - 511
EP - 517
BT - CLEF 2016
A2 - Balog, Krisztian
A2 - Cappellato, Linda
A2 - Ferro, Nicola
A2 - Macdonald, Craig
PB - RWTH Aachen University
CY - Aachen Germany
ER -