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.

EB00000002478K

Available

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