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Two dimensional compressive classifier for sparse images

Eftekhari, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/ICIP.2009.5414298
  3. Abstract:
  4. The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the information in the signal, with high probability. Following the success in signal reconstruction, compressive framework has recently proved useful in classification. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is studied. Findings are then employed to develop a 2D compressive classifier (2DCC) for sparse images. Finally, theoretical results are validated within a realistic experimental framework. ©2009 IEEE
  5. Keywords:
  6. Compressive sampling ; Random projections ; Retinal identification ; Image processing ; Imaging systems ; Ophthalmology ; Compressive sampling ; Concentration of measure ; High probability ; Image domain ; Linear projections ; Random projections ; Sparse images ; Theoretical result ; Classifiers
  7. Source: 2009 IEEE International Conference on Image Processing, ICIP 2009, Cairo, 7 November 2009 through 10 November 2009 ; 2009 , Pages 2137-2140 ; 15224880 (ISSN) ; 9781424456543 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5298785/?reload=true