A Fractal Geometry Based Method for Dataset Augmentation by Automatically Generation of Multi-View Images
Ryosuke YAMADA, Ryota Suzuki, Akio Nakamura, Hirokatsu Kataoka · Journal of the Japan Society for Precision Engineering · 2021
Large-scale image datasets are constructed with a significant amount of time and human effort, however, an image may have biases in the camera positions that render the target object. In this study, we propose a framework that automatically conducts 3D models, multi-view images, and even category definition. We automatically generate 3D models based on fractal geometry, which is the regularity behind natural phenomena and render images from multiple viewpoints. By following those generation processes, we can automatically construct a generally large-scale image dataset that takes into account the viewpoint position. The experimental results show that the classification accuracy is improved over the conventional baseline in the context of pre-training for image recognition tasks. We show that our proposed method provides an effective method for automatically constructing pre-training datasets for image recognition tasks.