Attention to Contour: A Contour-Guided Deep Network for Pollen Classification

Pollen classification plays an essential role in many fields such as medicine and palynology. Notably, manual pollen identification via observing key pollen information (e.g., their contours) is time-consuming and laborious. To date, deep learning methods can extract complex features in an end-to-end manner. However, deep learning based automatic classification methods on pollen grains are still rare, and their performances remain unsatisfactory owing to limitation of interference from irrelevant information (such as impurities and bubbles) and the lack of pollen attention. Based on the above considerations, we propose a contour-guided network called CG-Net, which contains three modules. Image pre-processing module first removes impurities and bubbles in pollen images according to the color information. Then, contour awareness module is designed to generate contour features and these features are served as attention maps for next module. Finally, contour guidance module weights the yielded contour attention maps to both original images and feature maps of different convolution layers, making the CNN focus on discriminative features of pollen grains. Extensive experiments are conducted on several real-world pollen datasets, and the results demonstrate the effectiveness of our proposed method with the accuracy and F1-score over 84%.