The Radon Cumulative Distribution Transform and Its Application to Image Classification

IEEE Trans Image Process. 2016 Feb;25(2):920-34. doi: 10.1109/TIP.2015.2509419. Epub 2015 Dec 17.


Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed. Most transforms in current use (e.g., Fourier, wavelet, and so on) are linear transforms and, by themselves, are unable to substantially simplify the representation of image classes for classification. Here, we describe a nonlinear, invertible, low-level image processing transform based on combining the well-known Radon transform for image data, and the 1D cumulative distribution transform proposed earlier. We describe a few of the properties of this new transform, and with both theoretical and experimental results show that it can often render certain problems linearly separable in a transform space.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Face / anatomy & histology
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Pattern Recognition, Automated / methods
  • Signal Processing, Computer-Assisted*