  Data augmentation greatly increases the amount of data obtained based on
labeled data to save on expenses and labor for data collection and labeling. We
present a new approach for data augmentation called nine-dot MLS (ND-MLS). This
approach is proposed based on the idea of image defor-mation. Images are
deformed based on control points, which are calculated by ND-MLS. The method
can generate over 2000 images for one exist-ing dataset in a short time. To
verify this data augmentation method, extensive tests were performed covering 3
main tasks of computer vision, namely, classification, detection and
segmentation. The results show that 1) in classification, 10 images per
category were used for training, and VGGNet can obtain 92% top-1 acc on the
MNIST dataset of handwritten digits by ND-MLS. In the Omniglot dataset, the
few-shot accuracy usu-ally decreases with the increase in character categories.
However, the ND-MLS method has stable performance and obtains 96.5 top-1 acc in
Res-Net on 100 different handwritten character classification tasks; 2) in
segmentation, under the premise of only ten original images, DeepLab obtains
93.5%, 85%, and 73.3% m_IOU(10) on the bottle, horse, and grass test datasets,
respectively, while the cat test dataset obtains 86.7% m_IOU(10) with the
SegNet model; 3) with only 10 original images from each category in object
detection, YOLO v4 obtains 100% and 97.2% bottle and horse detection,
respectively, while the cat dataset obtains 93.6% with YOLO v3. In summary,
ND-MLS can perform well on classification, object detec-tion, and semantic
segmentation tasks by using only a few data.
