  PCANet, as one noticeable shallow network, employs the histogram
representation for feature pooling. However, there are three main problems
about this kind of pooling method. First, the histogram-based pooling method
binarizes the feature maps and leads to inevitable discriminative information
loss. Second, it is difficult to effectively combine other visual cues into a
compact representation, because the simple concatenation of various visual cues
leads to feature representation inefficiency. Third, the dimensionality of
histogram-based output grows exponentially with the number of feature maps
used. In order to overcome these problems, we propose a novel shallow network
model, named as PCANet-II. Compared with the histogram-based output, the second
order pooling not only provides more discriminative information by preserving
both the magnitude and sign of convolutional responses, but also dramatically
reduces the size of output features. Thus we combine the second order
statistical pooling method with the shallow network, i.e., PCANet. Moreover, it
is easy to combine other discriminative and robust cues by using the second
order pooling. So we introduce the binary feature difference encoding scheme
into our PCANet-II to further improve robustness. Experiments demonstrate the
effectiveness and robustness of our proposed PCANet-II method.
