Radon cumulative distribution transform subspace modeling for image classification
This method addresses image classification problems for applications where computational resources or labeled data are limited, though it appears incremental as it builds on existing R-CDT techniques.
The authors tackled image classification by using the Radon Cumulative Distribution Transform (R-CDT) to simplify modeling of deformations like translation and scaling, resulting in a method that achieves competitive accuracies to state-of-the-art neural networks while being computationally efficient and requiring fewer training samples.
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.