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.
Item Type:
Article (Published)
Refereed:
Yes
Uncontrolled Keywords:
Restricted Boltzmann Machine; image local binary descriptors; aggregation techniques of feature vectors