Details

Autor(en) / Beteiligte

• Corchs, Silvia Elena
• Ciocca, Gianluigi
• Bricolo, Emanuela
• Gasparini, Francesca
• Shao, Feng

Titel

Predicting Complexity Perception of Real World Images

Ist Teil von

• PloS one, 2016-06, Vol.11 (6), p.e0157986-e0157986

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2016

Quelle

MEDLINE

Beschreibungen/Notizen

• The aim of this work is to predict the complexity perception of real world images. We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images.