XCSF for prediction on emotion induced by image based on dimensional theory of emotion

Po Ming Lee*, Yun Teng, Tzu-Chien Hsiao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Affective image classification problem is a problem aims on classifying images according to their affective characteristics of inducing human emotions. This paper extends the discrete state classification problem into a continuous function approximation problem by applying the experimental paradigm of dimensional emotion model. The Extended Classifier System for Function Approximation (XCSF) was applied to the problem and the results suggest that it outperforms linear regression (LR) in accomplishing this task. The obtained results also indicate that without using content based features of the images, the effects of individual difference can be relatively small.

Original languageEnglish
Title of host publicationGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion
Pages375-382
Number of pages8
DOIs
StatePublished - 20 Aug 2012
Event14th International Conference on Genetic and Evolutionary Computation, GECCO'12 - Philadelphia, PA, United States
Duration: 7 Jul 201211 Jul 2012

Publication series

NameGECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion

Conference

Conference14th International Conference on Genetic and Evolutionary Computation, GECCO'12
CountryUnited States
CityPhiladelphia, PA
Period7/07/1211/07/12

Keywords

  • Affective picture
  • Extended classifier system
  • Self-assessment manikin

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