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Proceedings Paper

Improved convolutional networks in forest species identification task
Author(s): Kar Fai Siew; Xin Jie Tang; Yong Haur Tay
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Paper Abstract

Forest species identification is a special case of texture classification problem that can be solved with hand-crafted features. Convolutional Networks (ConvNet) is able to learn features adaptively and it has achieved impressive result in complicated recognition tasks. This paper presents an improvement to ConvNet-based approach in1 for forest species identification. Due to the small amount of training data, we proposed the addition of dropout layer to ConvNet architecture and data augmentation to increase the size of training data. New classification process of combining the ConvNet outputs of each image patches is proposed. Our improved ConvNet-based method has achieved promising results.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104430C (19 June 2017);
Show Author Affiliations
Kar Fai Siew, Univ. Tunku Abdul Rahman (Malaysia)
Xin Jie Tang, Univ. Tunku Abdul Rahman (Malaysia)
Yong Haur Tay, Univ. Tunku Abdul Rahman (Malaysia)

Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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