Radon累积分布变换子空间建模用于图像分类
Radon cumulative distribution transform subspace modeling for image classification
摘要：
我们提出了一种新的监督图像分类方法适用于广泛的一类图像变形模型。该方法利用先前描述的用于图像数据的Radon累积分布变换（R-CDT），其数学性质被利用来以更适合于机器学习的形式表达图像数据。虽然某些操作，如平移，缩放和高阶变换是具有挑战性的模型在本机图像空间，我们的R-CDT可以捕捉这些变化，从而使相关的图像分类问题更容易解决。该方法-利用R-CDT空间中的最近子空间算法-实现简单，非迭代，没有超参数调整，计算效率高，标签效率高，并为许多类型的分类问题提供了具有竞争力的精度，以最先进的神经网络。除了测试精度性能之外，我们还在计算效率（可以在不使用GPU的情况下实现）、训练所需的训练样本数量以及分布外泛化方面显示了改进（相对于基于神经网络的方法）。可以在https://github.com/rohdelab/rcdt_ns_classifier上找到用于复制我们的结果的Python代码。
英文摘要：
We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method -- utilizing a nearest-subspace algorithm in R-CDT space -- is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems. In addition to the test accuracy performances, we show improvements (with respect to neural network-based methods) in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at https://github.com/rohdelab/rcdt_ns_classifier.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
