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Discriminative Deep Belief Network for Indoor Environment Classification Using Global Visual Features

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Abstract

Indoor environment classification, also known as indoor environment recognition, is a highly appreciated perceptual ability in mobile robots. In this paper, we present a novel approach which is centered on biologically inspired methods for recognition and representation of indoor environments. First, global visual features are extracted by using the GIST descriptor, and then we use the subsequent features for training the discriminative deep belief network (DDBN) classifier. DDBN employs a new deep architecture which is based on restricted Boltzmann machines (RBMs) and the joint density model. The back-propagation technique is used over the entire classifier to fine-tune the weights for an optimum classification. The acquired experimental results validate our approach as it performs well both in the real-world and in synthetic datasets and outperforms the Convolution Neural Networks (ConvNets) in terms of computational efficiency.

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Notes

  1. http://categorizingplaces.com/dataset.html

  2. https://keras.io/

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Acknowledgments

The authors would like to thank Mr. Mohammad Ali Keyvanrad of Laboratory for Intelligent Multimedia Processing (LIMP), Amirkabir University of Technology, Tehran, Iran for his discussions and Matlab Toolbox which helped in the improvement of the paper.

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Authors and Affiliations

  1. LIMIARF Laboratory, Faculty of Sciences Rabat, Mohammed V University in Rabat, Rabat, Morocco

    Nabila Zrira & El Houssine Bouyakhf

  2. Le2i, FRE CNRS 2005, Arts et Métiers, Université Bourgogne Franche-Comté, Dijon, France

    Haris Ahmad Khan

  3. The Norwegian Colour and Visual Computing Laboratory, NTNU-Norwegian University of Science and Technology, Gjøvik, Norway

    Haris Ahmad Khan

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  1. Nabila Zrira
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  3. El Houssine Bouyakhf
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Correspondence to Nabila Zrira.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all participants for which identifying information is included in this article.

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Zrira, N., Khan, H.A. & Bouyakhf, E.H. Discriminative Deep Belief Network for Indoor Environment Classification Using Global Visual Features. Cogn Comput 10, 437–453 (2018). https://doi.org/10.1007/s12559-017-9534-9

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