  The visual recognition of transitive actions comprising human-object
interactions is a key component for artificial systems operating in natural
environments. This challenging task requires jointly the recognition of
articulated body actions as well as the extraction of semantic elements from
the scene such as the identity of the manipulated objects. In this paper, we
present a self-organizing neural network for the recognition of human-object
interactions from RGB-D videos. Our model consists of a hierarchy of
Grow-When-Required (GWR) networks that learn prototypical representations of
body motion patterns and objects, accounting for the development of
action-object mappings in an unsupervised fashion. We report experimental
results on a dataset of daily activities collected for the purpose of this
study as well as on a publicly available benchmark dataset. In line with
neurophysiological studies, our self-organizing architecture exhibits higher
neural activation for congruent action-object pairs learned during training
sessions with respect to synthetically created incongruent ones. We show that
our unsupervised model shows competitive classification results on the
benchmark dataset with respect to strictly supervised approaches.
