Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.

ABSTRACT: 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 the R-CDT space - is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label

SUBMITTER: Shifat-E-Rabbi M 

PROVIDER: S-EPMC9032314 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature