# Deep convolutional filter banks for texture recognition and segmentation

arXiv: https://arxiv.org/abs/1411.6836 · HTML: https://mlsanity.com/paper/1411.6836/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2014.

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

- [MaPa: Text-driven Photorealistic Material Painting for 3D Shapes](https://mlsanity.com/paper/2404.17569/) (2024)
- [Materialistic: Selecting Similar Materials in Images](https://mlsanity.com/paper/2305.13291/) (2023)
- [Deep Features for training Support Vector Machine](https://mlsanity.com/paper/2104.03488/) (2021)
- [Fine-Grained Spatially Varying Material Selection in Images](https://mlsanity.com/paper/2506.09023/) (2025)

## Abstract (arXiv)

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture at- tributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, D-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. D-CNN substantially improves the state-of-the-art in texture, mate- rial and scene recognition. Our approach achieves 82.3% accuracy on Flickr material dataset and 81.1% accuracy on MIT indoor scenes, providing absolute gains of more than 10% over existing approaches. D-CNN easily trans- fers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, D-CNN can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at lo- calizing stuff categories and obtains state-of-the-art re- sults on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.