Abstract The article presents a novel approach to the challenge of real-time image classification with deep neural networks. The proposed architecture of the neural network exploits computationally efficient local binary descriptors and uses a Restricted Boltzmann Machine (RBM) as a feature space projection step so that the resulting depth of the deep neural network can be reduced. A Contrastive Divergence procedure is used both for RBM training and for feature projection. The resulting neural networks exhibit performance close to the current state of the art but are characterized by a small model memory footprint (i.e., number of parameters) and extremely efficient computational complexity (i.e, response time). The low number of parameters makes these architectures applicable in embedded systems with limited memory or reduced computational capabilities.

Url http://doras.dcu.ie/23983/1/VISUAL_COMPUTER%20%281%29.pdf

Collections Ireland -> Dublin City University -> Subject = Computer Science: Image processing
Ireland -> Dublin City University -> Publication Type = Article
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
Ireland -> Dublin City University -> Subject = Computer Science: Digital video
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning