# Deep filter banks for texture recognition, description, and segmentation

arXiv: https://arxiv.org/abs/1507.02620 · HTML: https://mlsanity.com/paper/1507.02620/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2015.

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

- [VisGraphNet: a complex network interpretation of convolutional neural features](https://mlsanity.com/paper/2108.12490/) (2021)
- [Fractal measures of image local features: an application to texture recognition](https://mlsanity.com/paper/2108.12491/) (2021)
- [Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2](https://mlsanity.com/paper/2203.04221/) (2022)
- [Texture Representation via Analysis and Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/2212.09983/) (2022)
- [Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis](https://mlsanity.com/paper/2104.00669/) (2021)
- [Multilayer deep feature extraction for visual texture recognition](https://mlsanity.com/paper/2208.10044/) (2022)
- [Texture image classification based on a pseudo-parabolic diffusion model](https://mlsanity.com/paper/2011.07173/) (2020)
- [Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets](https://mlsanity.com/paper/2410.19191/) (2024)
- [An application of a pseudo-parabolic modeling to texture image recognition](https://mlsanity.com/paper/2102.05001/) (2021)
- [U(1) Symmetry-breaking Observed in Generic CNN Bottleneck Layers](https://mlsanity.com/paper/2206.02220/) (2022)
- [Manga Rescreening with Interpretable Screentone Representation](https://mlsanity.com/paper/2306.04114/) (2023)
- [Coloring Deep CNN Layers with Activation Hue Loss](https://mlsanity.com/paper/2310.03911/) (2023)
- [Evidential Transformers for Improved Image Retrieval](https://mlsanity.com/paper/2409.01082/) (2024)
- [SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2409.06706/) (2024)
- [Patch-Based Spatial Authorship Attribution in Human-Robot Collaborative Paintings](https://mlsanity.com/paper/2602.17030/) (2026)
- [Chaotic Contrastive Learning for Robust Texture Classification](https://mlsanity.com/paper/2605.05012/) (2026)
- [A self-supervised learning approach to deep filter banks for texture recognition](https://mlsanity.com/paper/2605.27843/) (2026)
- [Where Does Texture Evidence Live in SAM? Features, Proposal Masks, and Texture Segmentation](https://mlsanity.com/paper/2606.14755/) (2026)
- [Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks](https://mlsanity.com/paper/2608.28524/) (2026)

## Abstract (arXiv)

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture representations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.