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Cognitive Computation

Erschienen in:

01.01.2018

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

verfasst von: Nabila Zrira, Haris Ahmad Khan, El Houssine Bouyakhf

Erschienen in: Cognitive Computation | Ausgabe 3/2018

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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.

Vorheriger Artikel The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry

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Titel: Discriminative Deep Belief Network for Indoor Environment Classification Using Global Visual Features
verfasst von: Nabila Zrira
Haris Ahmad Khan
El Houssine Bouyakhf
Publikationsdatum: 01.01.2018
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 3/2018
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-017-9534-9

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