# Using Filter Banks in Convolutional Neural Networks for Texture Classification

arXiv: https://arxiv.org/abs/1601.02919 · HTML: https://mlsanity.com/paper/1601.02919/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2016.

## Built on by (most cited first, 15 of 15)

- [Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2305.03944/) (2023)
- [Group-invariant max filtering](https://mlsanity.com/paper/2205.14039/) (2022)
- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [VisGraphNet: a complex network interpretation of convolutional neural features](https://mlsanity.com/paper/2108.12490/) (2021)
- [TFN: An Interpretable Neural Network with Time-Frequency Transform Embedded for Intelligent Fault Diagnosis](https://mlsanity.com/paper/2209.01992/) (2022)
- [Texture Representation via Analysis and Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/2212.09983/) (2022)
- [A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic Measures](https://mlsanity.com/paper/2102.06997/) (2021)
- [Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning](https://mlsanity.com/paper/2401.03070/) (2024)
- [Scorpion detection and classification systems based on computer vision and deep learning for health security purposes](https://mlsanity.com/paper/2105.15041/) (2021)
- [Multiscale Analysis for Improving Texture Classification](https://mlsanity.com/paper/2204.09841/) (2022)
- [Texture Extraction Methods Based Ensembling Framework for Improved Classification](https://mlsanity.com/paper/2206.04158/) (2022)
- [Large-Margin Representation Learning for Texture Classification](https://mlsanity.com/paper/2206.08537/) (2022)
- [VisTaNet: Attention Guided Deep Fusion for Surface Roughness Classification](https://mlsanity.com/paper/2209.08516/) (2022)
- [Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation](https://mlsanity.com/paper/2410.21562/) (2024)
- [GraphTEN: Graph Enhanced Texture Encoding Network](https://mlsanity.com/paper/2503.13991/) (2025)

## Abstract (arXiv)

Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.