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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/CGIV.2009.68
  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, particularly hypothesis testing. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is closely studied. Findings are then employed to develop a 2D compressive classifier (2D-CC) for sparse images. Finally, theoretical results are validated within a realistic experimental framework. © 2009 IEEE
  5. Keywords:
  6. Retinal identification ; Classification ; Compressive sampling ; Concentration of measure ; High probability ; Hypothesis testing ; Image domain ; Linear projections ; Random projections ; Sparse images ; Theoretical result ; Classifiers ; Learning systems ; Ophthalmology ; Visualization ; Computer graphics
  7. Source: Proceedings of the 2009 6th International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV2009, 11 August 2009 through 14 August 2009, Tianjin ; 2009 , Pages 402-405 ; 9780769537894 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5298785/?reload=true