# Multi-level Texture Encoding and Representation (MuLTER) based on Deep Neural Networks

arXiv: https://arxiv.org/abs/1905.09907 · HTML: https://mlsanity.com/paper/1905.09907/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

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

- [Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images](https://mlsanity.com/paper/2204.09854/) (2022)
- [Histogram Layers for Neural Engineered Features](https://mlsanity.com/paper/2403.17176/) (2024)
- [Learning-based Compression for Material and Texture Recognition](https://mlsanity.com/paper/2104.10065/) (2021)
- [DNN-Compressed Domain Visual Recognition with Feature Adaptation](https://mlsanity.com/paper/2305.08000/) (2023)

## Abstract (arXiv)

In this paper, we propose a multi-level texture encoding and representation network (MuLTER) for texture-related applications. Based on a multi-level pooling architecture, the MuLTER network simultaneously leverages low- and high-level features to maintain both texture details and spatial information. Such a pooling architecture involves few extra parameters and keeps feature dimensions fixed despite of the changes of image sizes. In comparison with state-of-the-art texture descriptors, the MuLTER network yields higher recognition accuracy on typical texture datasets such as MINC-2500 and GTOS-mobile with a discriminative and compact representation. In addition, we analyze the impact of combining features from different levels, which supports our claim that the fusion of multi-level features efficiently enhances recognition performance. Our source code will be published on GitHub (https://github.com/olivesgatech).