Intelligent Identification of Traditional Chinese Medicine Materials Based on Multi-feature Extraction and Pattern Recognition

DOI

10.2991/masta-19.2019.66How to use a DOI?

Keywords

Traditional Chinese Medicine (TCM) material, Feature extraction, Image recognition, K-Nearest Neighbor (KNN), Support Vector Machine (SVM)

Abstract

A discussion about image pattern recognition for Tradition Chinese Medicine (TCM) materials was explained in this paper. 150 images of each category of TCM materials were gathered, in total of five categories. 80% of the images were distributed as training samples randomly and the other 20% were used to test the pattern recognition algorithms. A multi-feature vector for each image was proposed including textual features, shape features and category labels to train pattern recognition methods K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) and test the recognition rates. Statistics of average recognition rates were made and indicated that the methods could classified the chosen five categories of TCM materials significantly with the accuracy of around 70% in average, providing a new solution for TCM materials intelligent identification.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rong-rong Chen
AU  - Ying-jun Chen
PY  - 2019/07
DA  - 2019/07
TI  - Intelligent Identification of Traditional Chinese Medicine Materials Based on Multi-feature Extraction and Pattern Recognition
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 390
EP  - 395
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.66
DO  - 10.2991/masta-19.2019.66
ID  - Chen2019/07
ER  -