In this article, 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.

Bianco, S., Ciocca, G. (2015). User preferences modeling and learning for pleasing photo collage generation. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS AND APPLICATIONS, 12(1), 1-23 [10.1145/2801126].

User preferences modeling and learning for pleasing photo collage generation

BIANCO, SIMONE
Primo
;
CIOCCA, GIANLUIGI
Ultimo
2015

Abstract

In this article, 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.
Articolo in rivista - Articolo scientifico
Algorithms; Automatic collage creation; Design; Experimentation; G.1.6 [mathematics of computing]: optimization; I.2.6 [artificial intelligence]: learning - parameter learning; I.4.0 [image processing and computer vision]: general; I.4.9 [image processing and computer vision]: applications; Optimization algorithm; Performance; Preference modeling; Subjective experiment; User modeling; Visual features extraction;
Automatic collage creation, optimization algorithm, preference modeling, subjective experiment, user modeling, visual features extraction
English
2015
12
1
1
23
reserved
Bianco, S., Ciocca, G. (2015). User preferences modeling and learning for pleasing photo collage generation. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS AND APPLICATIONS, 12(1), 1-23 [10.1145/2801126].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/88774
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