  This paper presents a new concept formation approach that supports the
ability to incrementally learn and predict labels for visual images. This work
integrates the idea of convolutional image processing, from computer vision
research, with a concept formation approach that is based on psychological
studies of how humans incrementally form and use concepts. We experimentally
evaluate this new approach by applying it to an incremental variation of the
MNIST digit recognition task. We compare its performance to Cobweb, a concept
formation approach that does not support convolutional processing, as well as
two convolutional neural networks that vary in the complexity of their
convolutional processing. This work represents a first step towards unifying
modern computer vision ideas with classical concept formation research.
