  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.
