  In this paper we consider how to automatically create pleasing photo collages
created by placing a set of images on a limited canvas area. The task is
formulated as an optimization problem. Differently from existing
state-of-the-art approaches, we here exploit subjective experiments to model
and learn pleasantness from user preferences. To this end, we design an
experimental framework for the identification of the criteria that need to be
taken into account to generate a pleasing photo collage. Five different
thematic photo datasets are used to create collages using state-of-the-art
criteria. A first subjective experiment where several subjects evaluated the
collages, emphasizes that different criteria are involved in the subjective
definition of pleasantness. We then identify new global and local criteria and
design algorithms to quantify them. The relative importance of these criteria
are automatically learned by exploiting the user preferences, and new collages
are generated. To validate our framework, we performed several psycho-visual
experiments involving different users. The results shows that the proposed
framework allows to learn a novel computational model which effectively encodes
an inter-user definition of pleasantness. The learned definition of
pleasantness generalizes well to new photo datasets of different themes and
sizes not used in the learning. Moreover, compared with two state of the art
approaches, the collages created using our framework are preferred by the
majority of the users.
