Content Specific Feature Learning for Fine-Grained Plant Classification
File version
Version of Record (VoR)
Author(s)
McCool, Chris
Sanderson, Conrad
Corke, Peter
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
Size
File type(s)
Location
Toulouse, France
Abstract
We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2015 plant task. Our system learns a content specific feature for various plant parts such as branch, leaf, fruit, flower 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.
Journal Title
Conference Title
Working Notes of CLEF 2015 - Conference and Labs of the Evaluation Forum
Book Title
Edition
Volume
1391
Issue
Thesis Type
Degree Program
School
Publisher link
DOI
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
- This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Item Access Status
Note
Access the data
Related item(s)
Subject
Computer vision
Machine learning not elsewhere classified
Persistent link to this record
Citation
Ge, Z; McCool, C; Sanderson, C; Corke, P, Content Specific Feature Learning for Fine-Grained Plant Classification, Working Notes of CLEF 2015 - Conference and Labs of the Evaluation Forum, 2015, 1391