胶囊网络作为一种新型深度学习网络,胶囊结构可以编码特征的姿态、纹理、色调等信息,对图像具有良好的纹理特征编码能力。针对胶囊网络的初级特征提取网络过于简单、空间特征表达能力不足的问题,提出了一种结合深度卷积神经网络特征表达能力与小波变换多分辨率分析能力的离散小波胶囊网络(DWTCapsNet)。首先,研究了胶囊网络在纹理图像分类应用中的可行性;其次,研究了DWTCapsNet各部分对胶囊网络分类性能提升的能力;最后,通过抗旋转和抗噪声实验分析了DWTCapsNet的鲁棒性。以分类准确率为模型评价标准,在常用纹理图像数据集上的实验结果表明,DWTCapsNet的分类准确率较高。

Capsule network is a new type of deep learning network, capsule structure can encode information such as posture, texture, hue, etc. of the feature, and has a good ability to express the texture feature of the image. Aiming at the problem that the primary feature extraction network of the capsule network is too simple and the feature expression ability is insufficient, a discrete wavelet capsule network (DWTCapsNet) that combines the feature expression capabilities of deep convolutional neural networks with wavelet transform multi-resolution analysis capabilities is propose in this work. First, the feasibility of the capsule network in the application of texture image classification is studied. Second, the ability of each part of DWTCapsNet on the improvement of capsule network classification performance is studied. Finally, the robustness of DWTCapsNet is analyzed through anti-rotation and anti-noise experiments. The classification accuracy is used as the standard model evaluation criteria, and the experimental results on the commonly used texture image data sets show that the classification accuracy of DWTCapsNet is higher.