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 labels are given at the image level and are based on non-natural images.
“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]
The Introduction says the 5,640 texture images were annotated with attributes, supporting image-level labels. But it says the images were downloaded from the Internet and captured “in the wild,” rather than collected in a laboratory; the Summary describes the task as recognizing attributes in natural images. This contradicts the claim that the labels are based on non-natural images. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredAnnotations from different perceptual levels are heterogeneous; ADE20K has pixel-wise annotations while DTD texture annotations are image-level.
“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]
Section 2, “The describable texture dataset” (page not specified), describes DTD as a collection of texture images annotated with attributes, supporting the image-level DTD example. The cited paper does not address ADE20K or its pixel-wise annotations, so it does not support the full comparison. (LLM, conf=0.88)
Experimental — accuracy not yet measuredVarious image datasets are constructed only for specific tasks, such as ADE20K for scene parsing, DTD for texture recognition, and OpenSurfaces for material and surface recognition.
“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]
There is no single image dataset annotated with all levels of visual information.
“First, there is no single image dataset annotated with all levels of visual information.”
From Unified Perceptual Parsing for Scene Understanding (2018) · cites this paper as [4]
The Introduction describes a dataset of texture images annotated with 47 texture attributes, and the Related work section discusses other texture and material datasets. Neither passage says that no image dataset is annotated at all levels of visual information. Searches for that assertion and for datasets annotated at multiple visual levels found no supporting passage in our copy; page numbers were not provided. (LLM, conf=0.72)
Experimental — accuracy not yet measuredBroden+ contains 152 part classes, 26 material classes, and 47 texture classes.
“| part | 152 | ADE [2], Pascal-Part [28] | mIoU (bg) & pixel acc. |”
From Unified Perceptual Parsing for Scene Understanding (2018) · cites this paper as [4]
The Introduction describes 47 texture attributes in the paper’s own dataset, not texture classes in Broden+. Searches for Broden+ and the counts 152 and 26 found no passage addressing Broden+’s class counts. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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Intriguing Properties of Vision Transformers
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“We analyze the transferability of off-the-shelf features across several datasets including Aircraft [40], CUB [37], DTD [41], GTSRB [42], Fungi [43], Places365 [44] and iNaturalist [39].”
Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis
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RobustBench: a standardized adversarial robustness benchmark
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Diversified Texture Synthesis with Feed-Forward Networks
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Self-regulating Prompts: Foundational Model Adaptation without Forgetting
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SimMatch: Semi-supervised Learning with Similarity Matching
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Scaling Vision with Sparse Mixture of ExpertsPreprint
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Incremental Learning Through Deep Adaptation
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Explaining the black-box model: A survey of local interpretation methods for deep neural networks
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Scaling Out-of-Distribution Detection for Real-World SettingsPreprint
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Improved Few-Shot Visual Classification
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What does a platypus look like? Generating customized prompts for zero-shot image classification
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Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization
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RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
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Deep learning for image inpainting: A survey
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C-CNN: Contourlet Convolutional Neural Networks
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Implicit Generation and Modeling in Energy-Based ModelsPreprint
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Boosting Contrastive Self-Supervised Learning with False Negative Cancellation
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- 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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