  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.
