  In Visual Semantics we study how humans build mental representations, i.e.,
concepts , of what they visually perceive. We call such concepts, substance
concepts. In this paper we provide a theory and an algorithm which learns
substance concepts which correspond to the concepts, that we call
classification concepts , that in Lexical Semantics are used to encode word
meanings. The theory and algorithm are based on three main contributions: (i)
substance concepts are modeled as visual objects , namely sequences of similar
frames, as perceived in multiple encounters ; (ii) substance concepts are
organized into a visual subsumption hierarchy based on the notions of Genus and
Differentia that resemble the notions that, in Lexical Semantics, allow to
construct hierarchies of classification concepts; (iii) the human feedback is
exploited not to name objects, as it has been the case so far, but, rather, to
align the hierarchy of substance concepts with that of classification concepts.
The learning algorithm is implemented for the base case of a hierarchy of depth
two. The experiments, though preliminary, show that the algorithm manages to
acquire the notions of Genus and Differentia with reasonable accuracy, this
despite seeing a small number of examples and receiving supervision on a
fraction of them.
