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

Forum topic · 小凯 · 2026-09-01

Summary

A paper by Arun D. Kulkarni (arXiv:2608.28524) proposes a hybrid feature fusion framework, DWT_AlexNet_DNN, for texture image classification. The approach addresses the limitations of both handcrafted features and deep learning models: handcrafted features capture local texture characteristics but struggle with complex visual patterns, while deep models learn discriminative representations automatically but may not fully exploit the multiscale spatial-frequency information inherent in texture images. The framework combines Discrete Wavelet Transform (DWT) features with deep features extracted using AlexNet, feeding the fused representation into a deep neural network classifier. Texture classification is important for applications including industrial inspection, medical image analysis, remote sensing, and object recognition.

Overview

Research Area: Computer Vision (CV) Author: Arun D. Kulkarni Published: 2026-08-28 arXiv: 2608.28524

Abstract

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.

Key Ideas

Motivation: Handcrafted features and deep features are complementary — the former capture local texture statistics, the latter learn high-level discriminative representations.
Approach: Fuse DWT-derived multiscale spatial-frequency features with AlexNet deep features.
Applications: Industrial inspection, medical image analysis, remote sensing, and object recognition.

*Auto-collected on 2026-09-01.*