  An essential aspect of texture analysis is the extraction of features that
describe the distribution of values in local, spatial regions. We present a
localized histogram layer for artificial neural networks. Instead of computing
global histograms as done previously, the proposed histogram layer directly
computes the local, spatial distribution of features for texture analysis and
parameters for the layer are estimated during backpropagation. We compare our
method with state-of-the-art texture encoding methods such as the Deep Encoding
Network Pooling, Deep Texture Encoding Network, Fisher Vector convolutional
neural network, and Multi-level Texture Encoding and Representation on three
material/texture datasets: (1) the Describable Texture Dataset; (2) an
extension of the ground terrain in outdoor scenes; (3) and a subset of the
Materials in Context dataset. Results indicate that the inclusion of the
proposed histogram layer improves performance. The source code for the
histogram layer is publicly available:
https://github.com/GatorSense/Histogram_Layer.
