{
  "id": "2208.11532",
  "version": "v1",
  "published": "2022-08-24T13:26:53.000Z",
  "updated": "2022-08-24T13:26:53.000Z",
  "title": "A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)",
  "authors": [
    "Wen Yang",
    "Rui Wang",
    "Yanchao Zhang"
  ],
  "comment": "16 pages,13 figures",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-08-24T13:26:53.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "novel method",
      "dot moving",
      "handwritten character classification tasks",
      "original images",
      "cat test dataset"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 16,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}