# From BoW to CNN: Two Decades of Texture Representation for Texture Classification

arXiv: https://arxiv.org/abs/1801.10324 · HTML: https://mlsanity.com/paper/1801.10324/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2018.

## Built on by (most cited first, 30 of 40)

- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [Deep Texture-Aware Features for Camouflaged Object Detection](https://mlsanity.com/paper/2102.02996/) (2021)
- [Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling](https://mlsanity.com/paper/2202.04812/) (2022)
- [Lightweight Pixel Difference Networks for Efficient Visual Representation Learning](https://mlsanity.com/paper/2402.00422/) (2024)
- [Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2105.05013/) (2021)
- [Deep Face Fuzzy Vault: Implementation and Performance](https://mlsanity.com/paper/2102.02458/) (2021)
- [RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps](https://mlsanity.com/paper/2303.04554/) (2023)
- [Deep Learning for Scene Classification: A Survey](https://mlsanity.com/paper/2101.10531/) (2021)
- [Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR](https://mlsanity.com/paper/2304.00668/) (2023)
- [Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery](https://mlsanity.com/paper/2201.07124/) (2022)
- [Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition](https://mlsanity.com/paper/2304.03550/) (2023)
- [A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis](https://mlsanity.com/paper/2406.06136/) (2024)
- [A Study on the Generality of Neural Network Structures for Monocular Depth Estimation](https://mlsanity.com/paper/2301.03169/) (2023)
- [Histogram Layers for Neural Engineered Features](https://mlsanity.com/paper/2403.17176/) (2024)
- [Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery](https://mlsanity.com/paper/2101.03134/) (2021)
- [Median Pixel Difference Convolutional Network for Robust Face Recognition](https://mlsanity.com/paper/2205.15867/) (2022)
- [Texture Representation via Analysis and Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/2212.09983/) (2022)
- [Hierarchical Material Recognition from Local Appearance](https://mlsanity.com/paper/2505.22911/) (2025)
- [A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic Measures](https://mlsanity.com/paper/2102.06997/) (2021)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms](https://mlsanity.com/paper/2206.12300/) (2022)
- [Histogram Layers for Synthetic Aperture Sonar Imagery](https://mlsanity.com/paper/2209.03878/) (2022)
- [PDCFNet: Enhancing Underwater Images through Pixel Difference Convolution](https://mlsanity.com/paper/2409.19269/) (2024)
- [Perceptual misalignment of texture representations in convolutional neural networks](https://mlsanity.com/paper/2604.01341/) (2026)
- [Texture image classification based on a pseudo-parabolic diffusion model](https://mlsanity.com/paper/2011.07173/) (2020)
- [Image Classifiers for Network Intrusions](https://mlsanity.com/paper/2103.07765/) (2021)
- [Deep Learning in the Field of Biometric Template Protection: An Overview](https://mlsanity.com/paper/2303.02715/) (2023)
- [Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain Adaptation](https://mlsanity.com/paper/2303.10960/) (2023)
- [VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings](https://mlsanity.com/paper/2503.06368/) (2025)
- [Multiscale Analysis for Improving Texture Classification](https://mlsanity.com/paper/2204.09841/) (2022)

## Abstract (arXiv)

Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention. Since 2000, texture representations based on Bag of Words (BoW) and on Convolutional Neural Networks (CNNs) have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 200 major publications are cited in this survey covering different aspects of the research, which includes (i) problem description; (ii) recent advances in the broad categories of BoW-based, CNN-based and attribute-based methods; and (iii) evaluation issues, specifically benchmark datasets and state of the art results. In retrospect of what has been achieved so far, the survey discusses open challenges and directions for future research.