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

Paper recorded by Signals 4 on 2026-08-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31

Category: cs.AI · 人工智能 · first seen 2026-08-31

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 representation

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#288 most recent of 300 cs.AI papers we have recorded · ↑ newer: InstructMesh: Selective Refinement of Generative 3D Models for Fabrica · ↓ older: When Robots Mishear Us: Mapping the Safety Risks of Voice-Controlled E
Cite this page: Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks: the #288 most recent of 300 cs.AI papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/texture-image-classification-using-dwt-alexnet-feature-fusion-and-deep-neural-ne.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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