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Direct classification from compressively sensed images via deep Boltzmann machine

Published on Nov 1, 2016 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
· DOI :10.1109/ACSSC.2016.7869080
Henry Braun6
Estimated H-index: 6
,
Pavan Turaga24
Estimated H-index: 24
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
Abstract
We examine a potential technique of performing a classification task based on compressively sensed (CS) data, skipping a computationally expensive reconstruction step. A deep Boltzmann machine is trained on a compressive representation of MNIST handwritten digit data, using a random orthoprojector sensing matrix. The network is first pre-trained on uncompressed data in order to learn the structure of the dataset. The outer network layers are then optimized using backpropagation. We find this approach achieves a 1.21% test data error rate at a sensing rate of 0.4, compared to a 0.99% error rate for non-compressive data.
  • References (16)
  • Citations (3)
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References16
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Given its importance in a wide variety of machine vision applications, extending high-speed object detection and recognition beyond the visible spectrum in a cost-effective manner presents a significant technological challenge. As a step in this direction, we developed a novel approach for target image classification using a compressive sensing architecture. Here we report the first implementation of this approach utilizing the compressive single-pixel camera system. The core of our approach res...
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#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
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The compressive sensing paradigm holds promise for more cost-effective imaging outside of the visible range, particularly in infrared wavelengths. However, the process of reconstructing compressively sensed images remains computationally expensive. The proof-of-concept tracker described here uses a particle filter with a likelihood update based on a “smashed filter” which estimates correlation directly, avoiding the reconstruction step. This approach leads to increased noise in correlation estim...
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#1Jeremy Vila (OSU: Ohio State University)H-Index: 8
#2Philip Schniter (OSU: Ohio State University)H-Index: 40
When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was a priori known, then one could use computationally efficient approximate message passing (AMP) techniques for nearly minimum MSE (MMSE) recovery. In practice, however, the distribution is unknown, motivating the use of robust algorithms like LASSO-which is nearly minimax optimal...
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Although considerable effort has been devoted to the problem of reconstructing compressively sensed video, no existing algorithm achieves results comparable to commonly available video compression methods such as H.264. One possible avenue for improving compressively sensed video reconstruction is the use of optical flow information. Current efforts reported in the literature have not fully utilized optical flow information, instead focusing on limited cases such as stationary backgrounds with s...
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We consider the problem of recovering a matrix M that is the sum of a low-rank matrix L and a sparse matrix S from a small set of linear measurements of the form y = A(M)= A(L + S). This model subsumes three important classes of signal recovery problems: compressive sensing, affine rank minimization, and robust principal component analysis. We propose a natural optimization problem for signal recovery under this model and develop a new greedy algorithm called SpaRCS to solve it. Empirically, Spa...
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