基于多注意金字塔网络的中药识别
Multiple Attentional Pyramid Networks for Chinese Herbal Recognition
摘要：
中草药在传统中医学中起着至关重要的作用。由于识别粒度不同，只有经验丰富的专业人员才能准确识别。预计可以使用机器学习等新技术自动识别它们。然而，没有可用的中草药图像数据集。同时，目前还没有一种机器学习方法能够很好地处理中药图像识别问题。因此，本文从建立一个新的标准中药数据集开始。在此基础上，提出了一种新的用于中草药识别的注意力金字塔网络（APN），该网络同时引入了新颖的竞争注意力和空间协作注意力，并将其应用于中草药识别。APN可以自适应地对不同特征尺度的中药图像进行建模。最后，作为APN的一个新应用，提出了一个新的中草药识别框架。在我们构造的数据集上进行了实验，验证了我们的方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
