2DCS: Two dimensional random underdetermined projection for image representation and classification

We consider the feature extraction problem based on compressive sampling for supervised image classification. Inspired by recently emerged 1D compressive sampling (1DCS) and 2DPCA techniques, a novel 2D compressive sampling method, called 2DCS, using two random underdetermined projections, is proposed. 2DCS data could be effectively used for pattern representation. Moreover, original data could be exactly reconstructed from 2DCS compression. The proposed method is efficient for feature extraction and data compression, and, compared with 1DCS and 2DPCA, requires lower computational complexity. Combined with the sophisticated classifiers, the efficacy of supervised image classification could be improved. Experimental results show the superiorities of the proposed algorithm.