Applying LCS to affective image classification in spatial-frequency domain

Po Ming Lee; Tzu-Chien Hsiao

doi:10.1109/CEC.2014.6900620

Applying LCS to affective image classification in spatial-frequency domain

Po Ming Lee^*, Tzu-Chien Hsiao

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1690-1697
Number of pages	8
ISBN (Electronic)	9781479914883
DOIs	https://doi.org/10.1109/CEC.2014.6900620
State	Published - 16 Sep 2014
Event	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Publication series

Name	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Conference

Conference	2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14

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10.1109/CEC.2014.6900620

Cite this

Lee, P. M., & Hsiao, T.-C. (2014). Applying LCS to affective image classification in spatial-frequency domain. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1690-1697). Article 6900620 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2014.6900620

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title = "Applying LCS to affective image classification in spatial-frequency domain",

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.",

author = "Lee, {Po Ming} and Tzu-Chien Hsiao",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

year = "2014",

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doi = "10.1109/CEC.2014.6900620",

language = "English",

series = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

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Lee, PM & Hsiao, T-C 2014, Applying LCS to affective image classification in spatial-frequency domain. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900620, Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 1690-1697, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/CEC.2014.6900620

Applying LCS to affective image classification in spatial-frequency domain. / Lee, Po Ming; Hsiao, Tzu-Chien.
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1690-1697 6900620 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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Applying LCS to affective image classification in spatial-frequency domain

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