Evaluation of features for leaf classification in challenging conditions

Hall, David, McCool, Chris, Dayoub, Feras, Sunderhauf, Niko, & Upcroft, Ben (2015) Evaluation of features for leaf classification in challenging conditions. In IEEE Winter Conference on Applications of Computer Vision (WACV 2015), 6-9 January 2015, Big Island, Hawaii, USA.


Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-theart performance with an average accuracy of 97:3%�0:6% compared to traditional features which obtain an average accuracy of 91:2%�1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5:7% for all of the evaluated condition variations.

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2 citations in Scopus
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ID Code: 78723
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 IEEE
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Deposited On: 17 Nov 2014 23:07
Last Modified: 05 May 2015 07:48

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