Predicting Complexity Perception of Real World Images.

ABSTRACT: 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 f

SUBMITTER: Corchs SE 

PROVIDER: S-EPMC4919018 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature