  We are interested in aligning how people think about objects and what
machines perceive, meaning by this the fact that object recognition, as
performed by a machine, should follow a process which resembles that followed
by humans when thinking of an object associated with a certain concept. The
ultimate goal is to build systems which can meaningfully interact with their
users, describing what they perceive in the users' own terms. As from the field
of Lexical Semantics, humans organize the meaning of words in hierarchies where
the meaning of, e.g., a noun, is defined in terms of the meaning of a more
general noun, its genus, and of one or more differentiating properties, its
differentia. The main tenet of this paper is that object recognition should
implement a hierarchical process which follows the hierarchical semantic
structure used to define the meaning of words. We achieve this goal by
implementing an algorithm which, for any object, recursively recognizes its
visual genus and its visual differentia. In other words, the recognition of an
object is decomposed in a sequence of steps where the locally relevant visual
features are recognized. This paper presents the algorithm and a first
evaluation.
