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


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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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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Correspondence to Nabila Zrira.

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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).

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  • Indoor environment
  • GIST descriptor
  • Discriminative Deep Belief Network (DDBN)
  • Back-propagation technique