# Deep Texture Manifold for Ground Terrain Recognition

arXiv: https://arxiv.org/abs/1803.10896 · HTML: https://mlsanity.com/paper/1803.10896/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 30 of 32)

- [Position, Padding and Predictions: A Deeper Look at Position Information in CNNs](https://mlsanity.com/paper/2101.12322/) (2021)
- [A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing](https://mlsanity.com/paper/2207.10614/) (2022)
- [Learning Gabor Texture Features for Fine-Grained Recognition](https://mlsanity.com/paper/2308.05396/) (2023)
- [RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps](https://mlsanity.com/paper/2303.04554/) (2023)
- [RCA: Ride Comfort-Aware Visual Navigation via Self-Supervised Learning](https://mlsanity.com/paper/2207.14460/) (2022)
- [VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain](https://mlsanity.com/paper/2209.07725/) (2022)
- [Improving filling level classification with adversarial training](https://mlsanity.com/paper/2102.04057/) (2021)
- [Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images](https://mlsanity.com/paper/2204.09854/) (2022)
- [Compositional Neural Textures](https://mlsanity.com/paper/2404.12509/) (2024)
- [Kuramoto Orientation Diffusion Models](https://mlsanity.com/paper/2509.15328/) (2025)
- [Histogram Layers for Neural Engineered Features](https://mlsanity.com/paper/2403.17176/) (2024)
- [Hierarchical Material Recognition from Local Appearance](https://mlsanity.com/paper/2505.22911/) (2025)
- [Learning-based Compression for Material and Texture Recognition](https://mlsanity.com/paper/2104.10065/) (2021)
- [Learnable Adaptive Cosine Estimator (LACE) for Image Classification](https://mlsanity.com/paper/2110.05324/) (2021)
- [Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis](https://mlsanity.com/paper/2404.16268/) (2024)
- [Texture image classification based on a pseudo-parabolic diffusion model](https://mlsanity.com/paper/2011.07173/) (2020)
- [Data augmentation with mixtures of max-entropy transformations for filling-level classification](https://mlsanity.com/paper/2203.04027/) (2022)
- [Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information](https://mlsanity.com/paper/2204.14224/) (2022)
- [Improving Contrastive Learning on Visually Homogeneous Mars Rover Images](https://mlsanity.com/paper/2210.09234/) (2022)
- [Learning a Pedestrian Social Behavior Dictionary](https://mlsanity.com/paper/2212.01426/) (2022)
- [Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis](https://mlsanity.com/paper/2305.06200/) (2023)
- [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)
- [VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings](https://mlsanity.com/paper/2503.06368/) (2025)
- [Enhancing Martian Terrain Recognition with Deep Constrained Clustering](https://mlsanity.com/paper/2503.17633/) (2025)
- [Neighborhood Feature Pooling for Remote Sensing Image Classification](https://mlsanity.com/paper/2510.25077/) (2025)
- [How to Relieve Distribution Shifts in Semantic Segmentation for Off-Road Environments](https://mlsanity.com/paper/2605.29599/) (2026)
- [Texture Extraction Methods Based Ensembling Framework for Improved Classification](https://mlsanity.com/paper/2206.04158/) (2022)
- [DNN-Compressed Domain Visual Recognition with Feature Adaptation](https://mlsanity.com/paper/2305.08000/) (2023)
- [Socially Cognizant Robotics for a Technology Enhanced Society](https://mlsanity.com/paper/2310.18303/) (2023)
- [Empirical curvelet based Fully Convolutional Network for supervised texture image segmentation](https://mlsanity.com/paper/2410.21562/) (2024)

## Abstract (arXiv)

We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. Recognition of ground terrain is an important task in establishing robot or vehicular control parameters, as well as for localization within an outdoor environment. The architecture of DEP integrates orderless texture details and local spatial information and the performance of DEP surpasses state-of-the-art methods for this task. The GTOS database (comprised of over 30,000 images of 40 classes of ground terrain in outdoor scenes) enables supervised recognition. For evaluation under realistic conditions, we use test images that are not from the existing GTOS dataset, but are instead from hand-held mobile phone videos of similar terrain. This new evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground terrain such as grass, gravel, asphalt and sand. The resultant network shows excellent performance not only for GTOS-mobile, but also for more general databases (MINC and DTD). Leveraging the discriminant features learned from this network, we build a new texture manifold called DEP-manifold. We learn a parametric distribution in feature space in a fully supervised manner, which gives the distance relationship among classes and provides a means to implicitly represent ambiguous class boundaries. The source code and database are publicly available.