# Histogram Layers for Neural Engineered Features

arXiv: https://arxiv.org/abs/2403.17176 · HTML: https://mlsanity.com/paper/2403.17176/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [Deep TEN: Texture Encoding Network](https://mlsanity.com/paper/1612.02844/) (2016)
- [From BoW to CNN: Two Decades of Texture Representation for Texture Classification](https://mlsanity.com/paper/1801.10324/) (2018)
- [Local Binary Convolutional Neural Networks](https://mlsanity.com/paper/1608.06049/) (2016)
- [Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2305.03944/) (2023)
- [Deep Texture Manifold for Ground Terrain Recognition](https://mlsanity.com/paper/1803.10896/) (2018)
- [Histogram Layers for Texture Analysis](https://mlsanity.com/paper/2001.00215/) (2020)
- [Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification](https://mlsanity.com/paper/1706.01171/) (2017)
- [Multi-level Texture Encoding and Representation (MuLTER) based on Deep Neural Networks](https://mlsanity.com/paper/1905.09907/) (2019)
- [Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation](https://mlsanity.com/paper/1904.01014/) (2019)
- [Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification](https://mlsanity.com/paper/2012.15764/) (2020)
- [Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation](https://mlsanity.com/paper/2110.07433/) (2021)
- [PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study](https://mlsanity.com/paper/2201.08002/) (2022)
- [Histogram Layers for Synthetic Aperture Sonar Imagery](https://mlsanity.com/paper/2209.03878/) (2022)
- [SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet](https://mlsanity.com/paper/1506.02509/) (2015)

## Built on by (most cited first, 4 of 4)

- [Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition](https://mlsanity.com/paper/2503.13763/) (2025)
- [Structural and Statistical Audio Texture Knowledge Distillation for Acoustic Classification](https://mlsanity.com/paper/2501.01921/) (2025)
- [LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction](https://mlsanity.com/paper/2601.17185/) (2026)
- [Dynamic Time Step Prediction in Inverse Heat Dissipation for Blur-Like Image Restoration Tasks](https://mlsanity.com/paper/2610.03942/) (2026)

## Abstract (arXiv)

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.