# Model-based learning of local image features for unsupervised texture segmentation

arXiv: https://arxiv.org/abs/1708.00180 · HTML: https://mlsanity.com/paper/1708.00180/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging](https://mlsanity.com/paper/2202.04680/) (2022)
- [Estimating Appearance Models for Image Segmentation via Tensor Factorization](https://mlsanity.com/paper/2208.07853/) (2022)
- [Variational multichannel multiclass segmentation using unsupervised lifting with CNNs](https://mlsanity.com/paper/2302.02214/) (2023)
- [Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation](https://mlsanity.com/paper/2410.21562/) (2024)

## Abstract (arXiv)

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.