research-article

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

Authors:

Tzu-Chien HsiaoAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 375 - 382

https://doi.org/10.1145/2330784.2330842

Published: 07 July 2012 Publication History

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.

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Cited By

Alslaity AOrji R(2022)Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directionsBehaviour & Information Technology10.1080/0144929X.2022.215638743:1(139-164)Online publication date: 16-Dec-2022
https://doi.org/10.1080/0144929X.2022.2156387
Balouchian PSafaei MForoosh H(2019)LUCFER: A Large-Scale Context-Sensitive Image Dataset for Deep Learning of Visual Emotions2019 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV.2019.00180(1645-1654)Online publication date: Jan-2019
https://doi.org/10.1109/WACV.2019.00180
Wu BJia JYang YZhao PTang JTian Q(2017)Inferring Emotional Tags From Social Images With User DemographicsIEEE Transactions on Multimedia10.1109/TMM.2017.265588119:7(1670-1684)Online publication date: Jul-2017
https://doi.org/10.1109/TMM.2017.2655881
Show More Cited By

Index Terms

XCSF for prediction on emotion induced by image based on dimensional theory of emotion
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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GECCO '12

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Alslaity AOrji R(2022)Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directionsBehaviour & Information Technology10.1080/0144929X.2022.215638743:1(139-164)Online publication date: 16-Dec-2022
https://doi.org/10.1080/0144929X.2022.2156387
Balouchian PSafaei MForoosh H(2019)LUCFER: A Large-Scale Context-Sensitive Image Dataset for Deep Learning of Visual Emotions2019 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV.2019.00180(1645-1654)Online publication date: Jan-2019
https://doi.org/10.1109/WACV.2019.00180
Wu BJia JYang YZhao PTang JTian Q(2017)Inferring Emotional Tags From Social Images With User DemographicsIEEE Transactions on Multimedia10.1109/TMM.2017.265588119:7(1670-1684)Online publication date: Jul-2017
https://doi.org/10.1109/TMM.2017.2655881
Li LLi SCao DLin D(2016)SentiNet: Mining Visual Sentiment from ScratchAdvances in Computational Intelligence Systems10.1007/978-3-319-46562-3_20(309-317)Online publication date: 7-Sep-2016
https://doi.org/10.1007/978-3-319-46562-3_20
Lee PTsui WHsiao T(2014)The influence of emotion on keyboard typing: an experimental study using visual stimuliBioMedical Engineering OnLine10.1186/1475-925X-13-8113:1Online publication date: 20-Jun-2014
https://doi.org/10.1186/1475-925X-13-81
Lee PHsiao T(2014)Applying LCS to affective image classification in spatial-frequency domain2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900620(1690-1697)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900620

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