Histogram Layers for Neural “Engineered” Features

Joshua Peeples, Salim Al Kharsa, Luke Saleh, Alina Zare

Abstract: 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 article, 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 feature representation for image classification. Experiments are presented on benchmark and real-world datasets.

External IDs:doi:10.1109/tai.2025.3593445