Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks
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 Discr
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- PossiblePossibly related (embedding) · 47%Hybrid deep learning framework enables real-time, high-precision prediction of midship bending moments in severe seas - EurekAlert! →
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Texture Image Classification Using DWT AlexNet Feature Fusio” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Arun D. Kulkarni →
“Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks”
