Multiple attentional pyramid networks for Chinese herbal recognition

被引：31

作者：

Xu, Yingxue ^{[1
]}

Wen, Guihua ^{[1
]}

Hu, Yang ^{[1
]}

Luo, Mingnan ^{[1
]}

Dai, Dan ^{[1
]}

Zhuang, Yishan ^{[1
]}

Hall, Wendy ^{[2
]}

机构：

[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China

[2] Univ Southampton, Web Sci Inst, Southampton, Hants, England

来源：

PATTERN RECOGNITION | 2021年 / 110卷 / 110期

基金：

美国国家科学基金会;

关键词：

Pyramid networks; Attention mechanism; Multi-scale features; Chinese herbal recognition; Chinese herbs image datasets;

D O I：

10.1016/j.patcog.2020.107558

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Chinese herbs play a critical role in Traditional Chinese Medicine. Due to different recognition granular-ity, they can be recognized accurately only by professionals with much experience. It is expected that they can be recognized automatically using new techniques like machine learning. However, there is no Chinese herbal image dataset available. Simultaneously, there is no machine learning method which can deal with Chinese herbal image recognition well. Therefore, this paper begins with building a new standard Chinese-Herbs dataset. Subsequently, a new Attentional Pyramid Networks (APN) for Chinese herbal recognition is proposed, where both novel competitive attention and spatial collaborative attention are proposed and then applied. APN can adaptively model Chinese herbal images with different feature scales. Finally, a new framework for Chinese herbal recognition is proposed as a new application of APN. Experiments are conducted on our constructed dataset and validate the effectiveness of our methods. (c) 2020 Published by Elsevier Ltd.

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