Open AccessPredicting Complexity Perception of Real World Images
Author(s) -
Silvia Corchs,
Gianluigi Ciocca,
Emanuela Bricolo,
Francesca Gasparini
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0157986
Subject(s) - measure (data warehouse) , computer science , artificial intelligence , weighting , computational complexity theory , pattern recognition (psychology) , set (abstract data type) , clutter , visual perception , computer vision , perception , algorithm , data mining , radar , medicine , telecommunications , neuroscience , biology , radiology , programming language
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.