  In this paper, we introduce a novel semantic description approach inspired on
Prototype Theory foundations. We propose a Computational Prototype Model (CPM)
that encodes and stores the central semantic meaning of objects category: the
semantic prototype. Also, we introduce a Prototype-based Description Model that
encodes the semantic meaning of an object while describing its features using
our CPM model. Our description method uses semantic prototypes computed by
CNN-classifications models to create discriminative signatures that describe an
object highlighting its most distinctive features within the category. Our
experiments show that: i) our CPM model (semantic prototype + distance metric)
is able to describe the internal semantic structure of objects categories; ii)
our semantic distance metric can be understood as the object visual typicality
score within a category; iii) our descriptor encoding is semantically
interpretable and significantly outperforms other image global encodings in
clustering and classification tasks.
