Work

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

Year: 2014

Type: conference-paper

Abstract: 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.

Source: QUT ePrints (Queensland University of Technology)

Authors: Niko Suenderhauf, Chris McCool, Ben Upcroft, Tristán Pérez

Institution: Queensland University of Technology

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Queensland Government

OpenAlex ID: W2108899438

FWCI: 2.268

Cites: 13

Cited by: 38

Related to: 10