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Describing Textures in the Wild
Open accessAbstract
Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks, in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.
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“Texture label , given at the image-level, is based on non-natural images.”
From Unified Perceptual Parsing for Scene Understanding (2018) · cites this paper as [4]
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“Next, annotations from different perceptual levels are heterogeneous. For example, ADE20K has pixel-wise annotations while the annotations for textures in the DTD are image-level.”
From Unified Perceptual Parsing for Scene Understanding (2018) · cites this paper as [4]
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“Various image datasets are constructed only for specific task, such as ADE20K for scene parsing [2], the Describe Texture Dataset (DTD) for texture recognition [4], and OpenSurfaces for material and surface recognition [6].”
From Unified Perceptual Parsing for Scene Understanding (2018) · cites this paper as [4]
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“First, there is no single image dataset annotated with all levels of visual information.”
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“| part | 152 | ADE [2], Pascal-Part [28] | mIoU (bg) & pixel acc. |”
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Metadata
- Authors
- Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., & Vedaldi, A.
- Affiliations
- University of Oxford (GB)
- Toyota Technological Institute (JP)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Stony Brook University (US)
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 3606-3613
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2047643928
- Cited by
- 2,438
- References
- 43
- Citations by year
- 2026: 318 · 2025: 476 · 2024: 435 · 2023: 328 · 2022: 168 · 2021: 218 · 2020: 120 · 2019: 110 · 2018: 89 · 2017: 82 · 2016: 56 · 2015: 35 · 2014: 3
- FWCI
- 31.54
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- other-oa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- National Science Foundation (1005411)
- Agence Nationale de la Recherche (ANR-10-JCJC-0205, ANR-10, ANR-10-JCJC)
- FP7 Ideas: European Research Council (228180)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- texture description, texture features, texture classification, material identification, DECAF, transfer learning, image understanding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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