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Proceedings Paper

Deep learning for image classification
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Paper Abstract

This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

Paper Details

Date Published: 10 June 2014
PDF: 10 pages
Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790T (10 June 2014); doi: 10.1117/12.2054045
Show Author Affiliations
Ryan McCoppin, Wright State Univ. (United States)
Mateen Rizki, Wright State Univ. (United States)


Published in SPIE Proceedings Vol. 9079:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V
Michael A. Kolodny, Editor(s)

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