Fine-grained plant classification using convolutional neural networks for feature extraction

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.

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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.

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ID Code: 82381
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Convolutional neural network, Extremely random forest, Plant classification
ISSN: 1613-0073
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 for the individual papers by the papers' authors.
Copyright Statement: Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
Deposited On: 10 Mar 2015 23:58
Last Modified: 12 Mar 2015 00:20

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