Abstract During large-scale simulations, intermediate data products such as image databases have become popular due to their low relative storage cost and fast in-situ analysis. Serving as a form of data reduction, these image databases have become more acceptable to perform data analysis on. We present an image-space detection and classification system for extracting vortices at multiple scales through wavelet-based filtering. A custom image-space descriptor is used to encode a large variety of vortex-types and a machine learning system is trained for fast classification of vortex regions. By combining a radial-based histogram descriptor, a bag of visual words feature descriptor, and a support vector machine, our results show that we are able to detect and classify vortex features at various sizes at multiple scales. Once trained, our framework enables the fast extraction of vortices on new, unknown image datasets for flow analysis.

中文翻译：

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通过机器学习对图像数据中的湍流特征进行多分辨率分类

摘要 在大规模模拟过程中，图像数据库等中间数据产品由于其相对存储成本低、原位分析速度快等特点而受到欢迎。作为数据简化的一种形式，这些图像数据库已变得更容易被接受进行数据分析。我们提出了一种图像空间检测和分类系统，用于通过基于小波的滤波在多个尺度上提取涡流。自定义图像空间描述符用于编码多种涡旋类型，并训练机器学习系统以快速分类涡旋区域。通过结合基于径向的直方图描述符、一袋视觉词特征描述符和支持向量机，我们的结果表明我们能够在多个尺度上检测和分类各种尺寸的涡旋特征。一经训练，