Discriminative Deep Belief Network for Indoor Environment Classification Using Global Visual Features
Résumé
Indoor environment classification, also known as indoor environment recognition, is a highly appreciated perceptualability in mobile robots. In this paper, we present a novel approach which is centered on biologically inspired methodsfor 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 insynthetic datasets and outperforms the Convolution Neural Networks (ConvNets) in terms of computational efficiency