@inproceedings{Pablo2017,
abstract = {Passive Millimeter Wave Images (PMMWIs) are being increasingly used to identify and localize objects concealed under clothing. Taking into account the quality of these images and the unknown position, shape, and size of the hidden objects, large data sets are required to build successful classification/detection systems. Kernel methods, in particular Gaussian Processes (GPs), are sound, flexible, and popular techniques to address supervised learning problems. Unfortunately, their computational cost is known to be prohibitive for large scale applications. In this work, we present a novel approach to PMMWI classification based on the use of Gaussian Processes for large data sets. The proposed methodology relies on linear approximations to kernel functions through random Fourier features. Model hyperparameters are learned within a variational Bayes inference scheme. Our proposal is well suited for real-time applications, since its computational cost at training and test times is much lower than the original GP formulation. The proposed approach is tested on a unique, large, and real PMMWI database containing a broad variety of sizes, types, and locations of hidden objects.},
author = {Morales, Pablo and Perez-Suay, Adrian and Molina, Rafael and Camps-Valls, Gustau and Katsaggelos, Aggelos K.},
booktitle = {2017 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2017.8296305},
isbn = {978-1-5090-2175-8},
issn = {15224880},
keywords = {Gaussian processes,Large scale classification,PMMWI,Random Fourier features,Variational inference},
month = {sep},
pages = {370--374},
publisher = {IEEE},
title = {{Passive millimeter wave image classification with large scale Gaussian processes}},
url = {http://ieeexplore.ieee.org/document/8296305/},
volume = {2017-Septe},
year = {2017}
}

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In this work, we present a novel approach to PMMWI classification based on the use of Gaussian Processes for large data sets. The proposed methodology relies on linear approximations to kernel functions through random Fourier features. Model hyperparameters are learned within a variational Bayes inference scheme. Our proposal is well suited for real-time applications, since its computational cost at training and test times is much lower than the original GP formulation. 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Taking into account the quality of these images and the unknown position, shape, and size of the hidden objects, large data sets are required to build successful classification/detection systems. Kernel methods, in particular Gaussian Processes (GPs), are sound, flexible, and popular techniques to address supervised learning problems. Unfortunately, their computational cost is known to be prohibitive for large scale applications. In this work, we present a novel approach to PMMWI classification based on the use of Gaussian Processes for large data sets. The proposed methodology relies on linear approximations to kernel functions through random Fourier features. Model hyperparameters are learned within a variational Bayes inference scheme. Our proposal is well suited for real-time applications, since its computational cost at training and test times is much lower than the original GP formulation. 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