mlsanityData to 7 Oct 2026

Describing Textures in the Wild

arXiv 1311.3618 ↗First posted 14 Nov 2013cs.CVcited by 2,110

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Cited by 534 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Patterns and textures are defining 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 analytical dimension in image understanding, we address the challenging problem of describing textures with semantic attributes. We identify a rich 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 the basis to seek for the best texture representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that the describable attributes are excellent texture descriptors, transferring between datasets and tasks; in particular, combined with IFV, they significantly outperform the state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.

Key facts. Cited by 2,110 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2013.

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Citing papers per year: 2020: 1; 2021: 153; 2022: 255; 2023: 378; 2024: 495; 2025: 487; 2026: 341.

2,110 papers.

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