Chinese Herbal Recognition based on Competitive Attentional Fusion of Multi-hierarchies Pyramid Features
This work addresses the domain-specific problem of Chinese herbal recognition for researchers and practitioners in traditional medicine, but it is incremental as it adapts existing CNN and attention mechanisms to a new dataset.
The authors tackled the problem of automatic Chinese herbal recognition by building a standard dataset and proposing a competitive attentional fusion pyramid network to model multi-level features, achieving superior performance validated through extensive experiments on their dataset.
Convolution neural netwotks (CNNs) are successfully applied in image recognition task. In this study, we explore the approach of automatic herbal recognition with CNNs and build the standard Chinese herbs datasets firstly. According to the characteristics of herbal images, we proposed the competitive attentional fusion pyramid networks to model the features of herbal image, which mdoels the relationship of feature maps from different levels, and re-weights multi-level channels with channel-wise attention mechanism. In this way, we can dynamically adjust the weight of feature maps from various layers, according to the visual characteristics of each herbal image. Moreover, we also introduce the spatial attention to recalibrate the misaligned features caused by sampling in features amalgamation. Extensive experiments are conducted on our proposed datasets and validate the superior performance of our proposed models. The Chinese herbs datasets will be released upon acceptance to facilitate the research of Chinese herbal recognition.