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IEEECGIV
2009
IEEE

Two Dimensional Compressive Classifier for Sparse Images

13 years 9 months ago
Two Dimensional Compressive Classifier for Sparse Images
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.
Armin Eftekhari, Hamid Abrishami Moghaddam, Massou
Added 24 May 2010
Updated 24 May 2010
Type Conference
Year 2009
Where IEEECGIV
Authors Armin Eftekhari, Hamid Abrishami Moghaddam, Massoud Babaie-Zadeh
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