Fine-grained plant classification using convolutional neural networks for feature extraction
| Contribuinte(s) |
Cappellato, Linda Ferro, Nicola Halvey, Martin Kraaij, Wessel |
|---|---|
| Data(s) |
2014
|
| Resumo |
We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014. |
| Identificador | |
| Publicador |
CEUR Workshop Proceedings |
| Relação |
http://ceur-ws.org/Vol-1180/CLEF2014wn-Life-SunderhaufEt2014.pdf Sunderhauf, Niko, McCool, Christopher, Upcroft, Ben, & Perez, Tristan (2014) Fine-grained plant classification using convolutional neural networks for feature extraction. In Cappellato, Linda, Ferro, Nicola, Halvey, Martin, & Kraaij, Wessel (Eds.) Working Notes for CLEF 2014 Conference, CEUR Workshop Proceedings, Sheffield, The United Kingdom, pp. 756-762. |
| Direitos |
Copyright 2014 for the individual papers by the papers' authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors. |
| Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
| Palavras-Chave | #Convolutional neural network #Extremely random forest #Plant classification |
| Tipo |
Conference Paper |
