发表机构
The University of Texas at Tyler（德克萨斯大学泰勒分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对纹理图像分类中手工特征与深度学习模型的不足，提出DWT_AlexNet_DNN混合特征融合框架，结合DWT特征与AlexNet深度特征实现纹理图像分类。
AI 中文摘要
纹理图像分类在计算机视觉应用中具有重要作用，涵盖工业检测、医学图像分析、遥感和目标识别等领域。手工特征可捕捉局部纹理特征，但在表示复杂视觉模式时能力有限；深度学习模型能自动学习判别性表示，但可能未充分利用纹理图像固有的多尺度空间频率信息。本文提出一种名为DWT_AlexNet_DNN的混合特征融合框架，将离散小波变换（DWT）特征与AlexNet提取的深度特征相结合，用于纹理图像分类。
英文摘要
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.
Comments15 pages, 15 figures
