Chinese herbal recognition by Spatial-/Channel-wise attention

Chinese herbal recognition has played an essential role in traditional Chinese medicine. Chinese herbal recognition is essentially an image classification task. However, unlike general image classification tasks, due to the particularity of Chinese medicine, traditional Chinese herbal medicine recognition pays more attention to the details of identification objects. However, the details that rely on the delicate paper size cannot be completely distinguished. In many cases, there is a large variety of different types of Chinese medicine. Paying too much attention to the details will cause the model to fall into partial dependence. So many times users need to use global features. In order to solve this challenge, we propose an effective way to model the mechanism by exploiting Spatial-/Channel-wise attention. Besides, we also leverage a dynamic adaptation mechanism that helps the model to balance global and detailed information. We verified the effectiveness of the proposed method via a series of experiments.

[1]  Lu Tan,et al.  Traditional Chinese Medicine Recognition Based on Target Detection , 2022, Evidence-based complementary and alternative medicine : eCAM.

[2]  J. Xin,et al.  Bupleurum Seeds Recognition with Attention Mechanism , 2021, 2021 China Automation Congress (CAC).

[3]  Xiaoze Yu,et al.  Big Data of Rhizome Chinese Herbal Medicine Harvester from the Perspective of Internet , 2021, 2021 International Wireless Communications and Mobile Computing (IWCMC).

[4]  Zixin Chen,et al.  An easy method for identifying 315 categories of commonly-used Chinese herbal medicines based on automated image recognition using AutoML platforms , 2020, Informatics in Medicine Unlocked.

[5]  Qian-Qian Li,et al.  Image Recognition of Chinese herbal pieces Based on Multi-task Learning Model , 2020, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[6]  Yuan-zhong Wang,et al.  Pattern recognition: An effective tool for quality assessment of herbal medicine based on chemical information , 2020, Journal of Chemometrics.

[7]  Yang Hu,et al.  Multiple Attentional Pyramid Networks for Chinese Herbal Recognition , 2020, Pattern Recognit..

[8]  Bohyung Han,et al.  Channel Attention Is All You Need for Video Frame Interpolation , 2020, AAAI.

[9]  Lin Wang,et al.  Classification of Chinese Herbal Medicine Using Combination of Broad Learning System and Convolutional Neural Network , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[10]  Xiangyu Zhang,et al.  ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.

[11]  Xiangjun Dong,et al.  Automatic Classification of Chinese Herbal Based on Deep Learning Method , 2018, 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[12]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Tianming Yang,et al.  Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis , 2013 .

[18]  Yi-zeng Liang,et al.  Chromatographic fingerprint analysis--a rational approach for quality assessment of traditional Chinese herbal medicine. , 2006, Journal of chromatography. A.

[19]  Chengzhuan Yang,et al.  Plant leaf recognition by integrating shape and texture features , 2021, Pattern Recognit..