# One-shot Texture Segmentation

arXiv: https://arxiv.org/abs/1807.02654 · HTML: https://mlsanity.com/paper/1807.02654/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data](https://mlsanity.com/paper/2403.03309/) (2024)
- [A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications](https://mlsanity.com/paper/2306.08814/) (2023)
- [Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation](https://mlsanity.com/paper/2410.21562/) (2024)

## Abstract (arXiv)

We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture. This task is designed to turn the problem of texture-based perceptual grouping into an objective benchmark. We show that it is straight-forward to generate large synthetic data sets for this task from a relatively small number of natural textures. In particular, this task can be cast as a self-supervised problem thereby alleviating the need for massive amounts of manually annotated data necessary for traditional segmentation tasks. In this paper we introduce and study two concrete data sets: a dense collage of textures (CollTex) and a cluttered texturized Omniglot data set. We show that a baseline model trained on these synthesized data is able to generalize to natural images and videos without further fine-tuning, suggesting that the learned image representations are useful for higher-level vision tasks.