UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Deep Filter Banks for Texture Recognition, Description, and Segmentation

Cimpoi, M; Maji, S; Kokkinos, I; Vedaldi, A; (2016) Deep Filter Banks for Texture Recognition, Description, and Segmentation. International Journal of Computer Vision , 118 (1) pp. 65-94. 10.1007/s11263-015-0872-3. Green open access

[img]
Preview
Text
art%3A10.1007%2Fs11263-015-0872-3.pdf - ["content_typename_Published version" not defined]

Download (12MB) | Preview

Abstract

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 represenations, 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.

Type: Article
Title: Deep Filter Banks for Texture Recognition, Description, and Segmentation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11263-015-0872-3
Publisher version: http://dx.doi.org/10.1007/s11263-015-0872-3
Language: English
Additional information: © The Author(s) 2015. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Texture and material recognition, Visual attributes, Convolutional neural networks, Filter banks, Fisher vectors, Datasets and benchmarks
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1527531
Downloads since deposit
43Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item