融合小波与深度特征，提升纹理图像分类精度
Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks
- 用小波变换提取多尺度纹理特征，结合AlexNet深层特征
- 在KTH-TIPS2b和CUReT数据集上准确率分别达98.7%和96.4%
- 适合需要高精度纹理识别的工业检测与医学影像场景
纹理图像分类在工业检测、医学图像分析、遥感和目标识别等计算机视觉应用中具有重要意义。手工特征可捕捉局部纹理特性，但对复杂视觉模式表征能力有限；而深度学习模型虽能自动学习判别性特征，却未必充分利用纹理图像中的多尺度空频信息。本文提出一种混合特征融合框架DWT_AlexNet_DNN，将离散小波变换（DWT）特征与AlexNet提取的深层特征相结合，用于纹理图像分类。实验在KTH-TIPS2b和CUReT数据集上验证，该方法显著提升了分类性能，最高准确率达98.7%（KTH-TIPS2b）和96.4%（CUReT），优于单一特征或传统融合方法。
原文摘要 · Abstract (English)
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.
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