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

Arun D. Kulkarni

cs.CVAug 28, 2026

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

Authors: Arun D. Kulkarni

Organizations: Computer Science Department, The University of Texas at Tyler, Tyler, TX 75799 USA

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.

Image Classification

Image classification assigns semantic labels to images, enabling recognition of objects, scenes, conditions, and fine-grained categories.

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Chaotic Contrastive Learning for Robust Texture Classification

Joao B Florindo

Institute of Mathematics, Statistics and Scientific Computing of the University of Campinas, Rua Sergio Buarque de Holanda, 651, Campinas, 13083-859, Sao Paulo, Brazil

Contrastive Learning Synthetic Data Augmentation Self-Supervised Visual Representation Learning Image Classification

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Texture classification is a pivotal task in computer vision, presenting unique challenges due to high inter-class similarity and the sensitivity of structural patterns to scale and illumination changes. While Convolutional Neural Networks (CNNs) and recent Vision Transformers have set performance benchmarks, they often require extensive labeled datasets or struggle to generalize across domains due to an over-reliance on color and shape features. This paper introduces a novel framework that synergizes Self-Supervised Learning (SSL) with deterministic chaotic dynamics. We propose a chaotic contrastive pre-training strategy, where pixel-wise chaotic maps, specifically Logistic, Tent, and Sine maps, act as non-linear data augmentation techniques. These chaotic perturbations, grounded in ergodic theory, force the network to learn topologically robust features by mimicking complex environmental noise and reflectance variations. Furthermore, we introduce an attention-based feature ensemble that fuses high-level semantic representations from a supervised large backbone with low-frequency structural features from a chaos-pretrained tiny encoder. Experimental results on six texture benchmarks (FMD, UMD, KTH-TIPS2-b, DTD, GTOS, and 1200Tex) demonstrate the superiority of the proposed method, outperforming state-of-the-art approaches and achieving promising accuracies on all the analyzed datasets.

Abstract

Image Classification

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