Sparse decomposition of two dimensional signals
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Two-dimensional random projection
Signal Processing
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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.