The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
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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In the citing paper
“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.”
From Unified Perceptual Parsing for Scene Understanding (2018) · cites this paper as [4]
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“| part | 152 | ADE [2], Pascal-Part [28] | mIoU (bg) & pixel acc. |”
From Unified Perceptual Parsing for Scene Understanding (2018) · cites this paper as [4]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
Platt, J. C. (1999) · Cited by 4,834
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Locality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206848 · Cited by 2,714
Aggregating local descriptors into a compact image representation
Jeǵou, H., Douze, M., Schmid, C., & Pérez, P. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540039 · Cited by 2,679
Research works printed but not linked: 1
- 10.1207/s15516709cog2102_4 · DOI
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Sorted by citations
Multi-view Convolutional Neural Networks for 3D Shape Recognition
Su, H., Maji, S., Kalogerakis, E., & Miller, E. G. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.114 · Cited by 3,608
Self-citationLearning to Prompt for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01653-1 · Cited by 3,304
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124
“To examine how our detectors would perform on objects with complex textures, we created simulated scenes in which the object of interest is given a texture from the Describable Textures Dataset (DTD) [4].”
MethodGenerative Image Inpainting with Contextual Attention
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00577 · Cited by 2,633
Bilinear CNN Models for Fine-Grained Visual Recognition
Lin, T., RoyChowdhury, A., & Maji, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.170 · Cited by 2,234
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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
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