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Deep learning has yielded remarkable outcomes in various domains. However,the challenge of requiring large-scale labeled samples still persists in deeplearning. Thus, data augmentation has been introduced as a critical strategy totrain deep learning models. However, data augmentation suffers from informationloss and poor performance in small sample environments. To overcome thesedrawbacks, we propose a feature augmentation method based on shape spacetheory, i.e., feature augmentation on Geodesic curve, called FAGC inbrevity.First, we extract features from the image with the neural networkmodel. Then, the multiple image features are projected into a pre-shape spaceas features. In the pre-shape space, a Geodesic curve is built to fit thefeatures. Finally, the many generated features on the Geodesic curve are usedto train the various machine learning models. The FAGC module can be seamlesslyintegrated with most machine learning methods. And the proposed method issimple, effective and insensitive for the small sample datasets.Severalexamples demonstrate that the FAGC method can greatly improve the performanceof the data preprocessing model in a small sample environment.

FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space