Chinese herbs play a critical role in Traditional Chinese Medicine. Due to
different recognition granularity, 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.
Yingxue Xu, Guihua Wen, Yang Hu, Mingnan Luo, Dan Dai, Yishan Zhuang, Wendy Hall