  Superpixel-based methodologies have become increasingly popular in computer
vision, especially when the computation is too expensive in time or memory to
perform with a large number of pixels or features. However, rarely is
superpixel segmentation examined within the context of deep convolutional
neural network architectures. This paper presents a novel neural architecture
that exploits the superpixel feature space. The visual feature space is
organized using superpixels to provide the neural network with a substructure
of the images. As the superpixels associate the visual feature space with parts
of the objects in an image, the visual feature space is transformed into a
structured vector representation per superpixel. It is shown that it is
feasible to learn superpixel features using capsules and it is potentially
beneficial to perform image analysis in such a structured manner. This novel
deep learning architecture is examined in the context of an image
classification task, highlighting explicit interpretability (explainability) of
the network's decision making. The results are compared against a baseline deep
neural model, as well as among superpixel capsule networks with a variety of
hyperparameter settings.
