论文标题
单基小波散射网络用于纹理图像分类
Monogenic Wavelet Scattering Network for Texture Image Classification
论文作者
论文摘要
散射变换网络（STN）具有与流行的卷积神经网络相似的结构，除了其使用预定义的卷积过滤器和少量层的结构，可以生成相对于小变形的输入信号的强大表示。我们提出了一个新型的单基础小波散射网络（MWSN），通过使用非线性模量和平均操作员替换标准STN中的2D Morlet小波滤波，通过一连串的单源小波滤波和平均操作员通过一连串的纹理图像分类。我们的MWSN可以提取具有可解释系数的有用的层次和方向性特征，可以通过PCA进一步压缩并馈入分类器。使用Curet纹理图像数据库，我们演示了MWSN优于标准STN的出色性能。该性能改善可以通过将1D分析性自然扩展到2D单基因性来解释。
The scattering transform network (STN), which has a similar structure as that of a popular convolutional neural network except its use of predefined convolution filters and a small number of layers, can generates a robust representation of an input signal relative to small deformations. We propose a novel Monogenic Wavelet Scattering Network (MWSN) for 2D texture image classification through a cascade of monogenic wavelet filtering with nonlinear modulus and averaging operators by replacing the 2D Morlet wavelet filtering in the standard STN. Our MWSN can extract useful hierarchical and directional features with interpretable coefficients, which can be further compressed by PCA and fed into a classifier. Using the CUReT texture image database, we demonstrate the superior performance of our MWSN over the standard STN. This performance improvement can be explained by the natural extension of 1D analyticity to 2D monogenicity.
