Correlation-Based Data Augmentation for Machine Learning and Its Application to Road Environment Recognition
Correlation-Based Data Augmentation for Machine Learning and Its Application to Road Environment Recognition
复制标题
基于相关性的机器学习数据增强及其在道路环境识别中的应用
DOI：
10.1109/tvt.2022.3167048
复制
发表时间：
2022
影响因子：
6.8
通讯作者：
Masako Omachi
中科院分区：
文献类型：
--
作者：
Shinichiro Omachi;Masako Omachi
机器学习的准确性在很大程度上取决于训练数据的数量和质量。然而，一般很难准备大量高质量的数据。为了生成多样化的图像数据，可以使用使用深度学习的图像生成技术，例如生成对抗网络。然而，由于这些方法需要大量的训练数据和大量的计算时间，因此它们不适合生成用于机器学习的训练数据。在这篇文章中，我们提出了一种基于训练数据的统计特性的图像数据增强模型。该方法利用一组图像计算出的相关性，将每幅图像划分为子区域。每个子区域的图像通过高斯混合建模，并通过基于该模型生成图像来进行数据增强。所提出的方法不需要大量的训练数据，并且可以在相对较短的计算时间内生成数据。该方法被应用到道路环境识别的任务。实验结果表明，该模型通过图像增强提高了图像分割的准确性.
The accuracy of machine learning depends largely on the quantity and quality of the training data. However, it is generally difficult to prepare a large number of high-quality data. To generate diverse image data, image generation techniques using deep learning, such as a generative adversarial network, can be used. However, because these methods require a large number of training data and a significant calculation time, they are unsuitable for generating training data for machine learning. In this article, we propose an image data augmentation model based on the statistical properties of the training data. With the proposed method, each image is divided into sub-regions based on the correlation calculated using a set of images. The image of each sub-region is modeled through a Gaussian mixture, and data augmentation is conducted by generating images based on this model. The proposed method does not require a large number of training data and can generate data within a relatively short calculation time. The proposed method is applied to the task of road environment recognition. The experiment results showed that the accuracy was improved through image augmentation using the proposed model.