[论文] Texture Image Classification Using DWT AlexNet Feature Fusion and Deep...

论文概要

研究领域: CV
作者: Arun D. Kulkarni
发布时间: 2026-08-28
arXiv: 2608.28524

中文摘要

纹理图像分类在计算机视觉应用中发挥着重要作用，包括工业检测、医学图像分析、遥感和物体识别。手工特征可以捕捉局部纹理特征，但可能有限地表示复杂视觉模式的能力。相比之下，深度学习模型自动学习判别性表示，但可能无法充分利用纹理图像固有的多尺度空间频率信息。本文提出了一个混合特征融合框架，称为DWT_AlexNet_DNN，它结合离散小波变换（DWT）特征和AlexNet提取的深度特征用于纹理图像分类。

原文摘要

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.

自动采集于 2026-09-01

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