# Histogram Layers for Texture Analysis

arXiv: https://arxiv.org/abs/2001.00215 · HTML: https://mlsanity.com/paper/2001.00215/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2020.

## Built on by (most cited first, 19 of 19)

- [S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens](https://mlsanity.com/paper/2309.04038/) (2023)
- [Histogram Layer Time Delay Neural Networks for Passive Sonar Classification](https://mlsanity.com/paper/2307.13788/) (2023)
- [Wise-SrNet: A Novel Architecture for Enhancing Image Classification by Learning Spatial Resolution of Feature Maps](https://mlsanity.com/paper/2104.12294/) (2021)
- [Histogram Layers for Neural Engineered Features](https://mlsanity.com/paper/2403.17176/) (2024)
- [The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression](https://mlsanity.com/paper/2106.02051/) (2021)
- [Quantification using Permutation-Invariant Networks based on Histograms](https://mlsanity.com/paper/2403.15123/) (2024)
- [Automatic Grid Updates for Kolmogorov-Arnold Networks using Layer Histograms](https://mlsanity.com/paper/2511.08570/) (2025)
- [Histogram Layers for Synthetic Aperture Sonar Imagery](https://mlsanity.com/paper/2209.03878/) (2022)
- [Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification](https://mlsanity.com/paper/2306.04037/) (2023)
- [Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module](https://mlsanity.com/paper/2309.00752/) (2023)
- [Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis](https://mlsanity.com/paper/2404.16268/) (2024)
- [Texture Classification Network Integrating Adaptive Wavelet Transform](https://mlsanity.com/paper/2404.05300/) (2024)
- [Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition](https://mlsanity.com/paper/2503.13763/) (2025)
- [Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classification](https://mlsanity.com/paper/2504.15214/) (2025)
- [Neighborhood Feature Pooling for Remote Sensing Image Classification](https://mlsanity.com/paper/2510.25077/) (2025)
- [Texture Extraction Methods Based Ensembling Framework for Improved Classification](https://mlsanity.com/paper/2206.04158/) (2022)
- [Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks](https://mlsanity.com/paper/2409.13881/) (2024)
- [GraphTEN: Graph Enhanced Texture Encoding Network](https://mlsanity.com/paper/2503.13991/) (2025)
- [Spatial Blindness in Whole-Slide Multiple Instance Learning](https://mlsanity.com/paper/2605.17449/) (2026)

## Abstract (arXiv)

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.