Histogram Layers for Texture Analysis

We present a histogram layer for artificial neural networks (ANNs). An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local spatial regions. The proposed histogram layer leverages the 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 (DEP) and Deep Texture Encoding Network (DeepTEN) on three texture datasets: (1) the Describable Texture Dataset (DTD); (2) an extension of the ground terrain in outdoor scenes (GTOS-mobile); (3) and a subset of the Materials in Context (MINC-2500) dataset. Results indicate that the inclusion of the proposed histogram layer improves performance. The source code for the histogram layer is publicly available.