Histogram Layers for Neural Engineered Features
Joshua Peeples Salim Al Kharsa Luke Saleh Alina Zare
In the computer vision literature, many effective histogram-based features
have been developed. These engineered features include local binary patterns
and edge histogram descriptors among others and they have been shown to be
informative features for a variety of computer vision tasks. In this paper, we
explore whether these features can be learned through histogram layers embedded
in a neural network and, therefore, be leveraged within deep learning
frameworks. By using histogram features, local statistics of the feature maps
from the convolution neural networks can be used to better represent the data.
We present neural versions of local binary pattern and edge histogram
descriptors that jointly improve the feature representation and perform image
classification. Experiments are presented on benchmark and real-world datasets.