Co-occurrence neural network

Convolutional Neural Networks (CNNs) became a very popular tool for image analysis. Convolutions are fast to compute and easy to store, but they also have some limitations. First, they are shift-invariant and, as a result, they do not adapt to different regions of the image. Second, they have a fixed spatial layout, so small geometric deformations in the layout of a patch will completely change the filter response. For these reasons, we need multiple filters to handle the different parts and variations in the input. We augment the standard convolutional tools used in CNNs with a new filter that addresses both issues raised above. Our filter combines two terms, a spatial filter and a term that is based on the co-occurrence statistics of input values in the neighborhood. The proposed filter is differentiable and can therefore be packaged as a layer in CNN and trained using back-propagation. We show how to train the filter as part of the network and report results on several data sets. In particular, we replace a convolutional layer with hundreds of thousands of parameters with a Co-occurrence Layer consisting of only a few hundred parameters with minimal impact on accuracy.