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Compressed domain image classification using a multi-rate neural network

2019-01-28
Yibo Xu, Kevin F. Kelly ( Department of Electrical & Computer Engineering, Rice University, Houston, USA)

Abstract

Compressed domain image classification aims to directly perform classification on compressive measurements generated from the single-pixel camera. While neural network approaches have achieved state-of-the-art performance, previous methods require training a dedicated network for each different measurement rate which is computationally costly. In this work, we present a general approach that endows a single neural network with multi-rate property for compressed domain classification where a single network is capable of classifying over an arbitrary number of measurements using dataset-independent fixed binary sensing patterns. We demonstrate the multi-rate neural network performance on MNIST and grayscale CIFAR-10 datasets. We also show that using the Partial Complete binary sensing matrix, the multi-rate network outperforms previous methods especially in the case of very few measurements.

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URL

http://arxiv.org/abs/1901.09983

PDF

http://arxiv.org/pdf/1901.09983


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