The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Deep Filter Banks for Texture Recognition, Description, and Segmentation
Open accessAbstract
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.
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Cites
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Self-citationObject recognition from local scale-invariant features
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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
A comparative study of texture measures with classification based on featured distributions
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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
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Metadata
- Authors
- Cimpoi, M., Maji, S., Kokkinos, I., & Vedaldi, A.
- Venue
- International Journal of Computer Vision
- Published
- 2016-01-09
- Volume
- 118
- Issue
- 1
- Pages
- 65-94
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W819977924
- Cited by
- 335
- References
- 22
- FWCI
- 37.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
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