Plant Classification Based on Gated Recurrent Unit

被引:1
作者
Lee, Sue Han [1 ]
Chang, Yang Loong [1 ]
Chan, Chee Seng [1 ]
Alexis, Joly [2 ]
Bonnet, Pierre [3 ]
Goeau, Herve [3 ]
机构
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Ctr Image & Signal Proc, Kuala Lumpur, Malaysia
[2] INRIA, Montpellier, France
[3] CIRAD Amap, Montpellier, France
来源
EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION (CLEF 2018) | 2018年 / 11018卷
关键词
Plant classification; Deep learning; Recurrent neural network;
D O I
10.1007/978-3-319-98932-7_16
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Classification of plants based on a multi-organ approach is very challenging due to the variability in shape and appearance in plant organs. Despite promising solutions built using convolutional neural network (CNN) for plant classification, the existing approaches do not consider the correspondence between different views captured of a plant. In fact, botanists usually observe and study simultaneously a plant from different vintage points, as a whole and also analyse different organs in order to disambiguate species. Driven by this insight, we introduce a new framework for plant structural learning using the recurrent neural network (RNN) approach. This novel approach supports classification based on a varying number of plant views composed of one or more organs of a plant, by optimizing the dependencies between them. We also present the qualitative results of our proposed models by visualizing the learned attention maps. To our knowledge, this is the first study to venture into such dependencies modeling and interpret the respective neural net for plant classification. Finally, we show that our proposed method outperforms the conventional CNN approach on the PlantClef2015 benchmark. The source code and models are available at https://github.com/cschan/Deep-Plant.
引用
收藏
页码:169 / 180
页数:12
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