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

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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2608.28524 [cs.CV]

(or arXiv:2608.28524v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.28524 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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