Image Classification with A Deep Network Model based on Compressive Sensing

2014/09/25 by Yufei Gan, Gan, Yufei, Tong Zhuo +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.1409.7307

arxiv created 2014/09/25 · arxiv updated 2014/09/26

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.

Related

• Cascaded Reconstruction Network for Compressive image sensing
• Compressed Learning: A Deep Neural Network Approach
• NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing
• LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing
• ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning