# Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks

arXiv: https://arxiv.org/abs/2608.28524 · HTML: https://mlsanity.com/paper/2608.28524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Deep TEN: Texture Encoding Network](https://mlsanity.com/paper/1612.02844/) (2016)
- [From BoW to CNN: Two Decades of Texture Representation for Texture Classification](https://mlsanity.com/paper/1801.10324/) (2018)
- [Wavelet Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1707.07394/) (2017)
- [Deep filter banks for texture recognition, description, and segmentation](https://mlsanity.com/paper/1507.02620/) (2015)
- [Texture Classification Network Integrating Adaptive Wavelet Transform](https://mlsanity.com/paper/2404.05300/) (2024)

## Abstract (arXiv)

Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote sensing, and object recognition. Handcrafted features can capture local texture characteristics but may have limited capability to represent complex visual patterns. In contrast, deep learning models automatically learn discriminative representations but may not fully exploit the multiscale spatial-frequency information inherent in texture images. This paper proposes a hybrid feature fusion framework, termed DWT_AlexNet_DNN, which combines Discrete Wavelet Transform (DWT) features with deep features extracted using AlexNet for texture image classification.