Applying LCS to affective image classification in spatial-frequency domain

Po Ming Lee*, Tzu-Chien Hsiao

*Corresponding author for this work

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

3 Scopus citations

Abstract

Affective image classification is a task aims on classifying images based on their affective characteristics of inducing human emotions. This study achieves the task by using Learning Classifier System (LCS) and spatial-frequency features. The model built by using LCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the LCS is compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used in classification tasks. The study presents user-independent results which indicate that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1690-1697
Number of pages8
ISBN (Electronic)9781479914883
DOIs
StatePublished - 16 Sep 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

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