Image classification with a deep network model based on compressive sensing
Signal Processing（2014）
Abstract
To simplify the parameter of the deep learning network, a cascaded compressive sensing model “CSNet” is implemented for image classification. Firstly, we use cascaded compressive sensing network to learn feature from the data. Secondly, CSNet generates the feature by binary hashing and block-wise histograms. Finally, a linear SVM classifier is used to classify these features. The experiments on the MNIST dataset indicate that higher classification accuracy can be obtained by this algorithm.
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Key words
image coding,cascaded compressive sensing model,block-wise histograms,deep learning,handwritten digit recognition,linear svm classifier,compressed sensing,image classification,compressive sensing,csnet,mnist dataset,binary hashing,deep network model,support vector machines,sensors
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