Fractal Geometry-Based Automatic Generation of Large-Scale Image Database for Pre-Training in 3D Object Recognition
Ryosuke YAMADA, Kazushige Okayasu, Akio Nakamura, Hirokatsu Kataoka · Journal of the Japan Society for Precision Engineering · 2021
We propose the generation method of large-scale image database for pre-training in 3D object recognition. The method is inspired from the principles of nature law. We adopt fractal geometry to represent the principles and build the Fractal Data Base random search (FractalDBrs). In contrast to traditional image database such as ImageNet, Iterated Function System (IFS) automatically generates large amount of image data to build the proposed FractalDBrs in short time without menial labors such as collecting and annotating images. In the experiments, we utilized the FractalDBrs and traditional databases; ImageNet, CIFAR100, Caltech256, or Places365, for pre-training in 3D object recognition with ModelNet40. The model pre-trained with FractalDBrs achieved the highest discrimination accuracy of 97.12% against the second highest accuracy of 96.43% with ImageNet. For reference, the model trained from scratch achieved 91.53% discrimination accuracy. We have verified the effectiveness of the proposed fractal geometry-based image database for pre-training in 3D object recognition.