Prediction of Human Emotions toward Abstract Images by Image Features and Eye Tracking Device

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กิติ์สุชาต พสุภา ภาณวี ฉัตรค้ำจุนเจริญ โชติรส วุฒิเลิศเดชา

Abstract

- Currently, emotion semantic search technology can support users to access data in the database. This can cover user’s desirable which focuses on emotion concept. Given an image to different users, users’ emotion stimulated by the image might be different due to different areas of interest. This paper presents a novel approach to increase the accuracy of emotion based image classification by combining eye movement data with basic image feature. The results show that combining eye movement data together with color feature can yield better classification performance than using color feature alone.

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How to Cite
พสุภาก., ฉัตรค้ำจุนเจริญภ., & วุฒิเลิศเดชาโ. (2015). Prediction of Human Emotions toward Abstract Images by Image Features and Eye Tracking Device. JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, 5(2), 1-8. https://doi.org/10.14456/jist.2015.7
Section
Research Article: Soft Computing (Detail in Scope of Journal)

References

1. L. Ye, P. Ogunbona and J. Wang “Image Content Annotation Based on Visual Features”, In: Proceeding of International Symposium on Multimedia (ISM’2006), 11-13 Dec 2006, San Diego, USA, pp. 62–69, 2006.

2. J. Laaksonen, M. Koskela, and E. Oja "PicSOM Self-organizing Image Retrieval with MPEG-7 Content Descriptors", IEEE Transactions on Neural Networks, 13(4), pp. 841–853, 2002.

3. R. Datta, J. Li, and J. Z. Wang, “Content-based Image Retrieval – Approaches and Trends of the New Age”, In: Proceedings of ACM International Workshop on Multimedia Information Retrieval (MIR’2015), 10-11 Nov 2005, Singapore, pp. 253–262, 2005.

4. W. Weining, Y. Yinlin, and J. Shengming, “Image Retrieval by Emotional Semantics: A Study of Emotional Space and Feature Extraction”, In: Proceeding of IEEE International Conference on Systems, Man and Cybernetics (SMC’2006), 8-11 Oct 2006, Taipei, Taiwan, pp. 3534–3539, 2006.

5. J. Machajdik, and A. Hanbury, “Affective Image Classitication Using Features Inspired by Psychology and Art Theory,” In: Proceeding of ACM International Conference on Multimedia (MM’2010), 25-29 Oct 2010, Firenze, Italy, pp. 83–92, 2010.

6. H. Zhang, E. Augilius, T. Honkela, J. Laaksonen, H. Gamper, and H. Alene, “Analyzing Emotional Semantics of Abstract Art Using Low-level Image Features”, In: Proceeding of International Symposium on Intelligent Data Analysis (IDA’2011), 29-31 Oct 2011, Porto, Portugal, pp. 413–423, 2011.

7. H. Zhang, M. Gönen, Z. Yang and E. Oja, “Predicting Emotional States of Images Using Bayesian Multiple Kernel Learning”, In: Proceeding of International Conference on Neural Information Processing (ICONIP’2013), 3-7 Nov 2013, Daegu, Korea, pp. 274–-282, 2013.

8. K. Pasupa, C. J. Saunders, S. Szedmak, A. Klami, S. Kaski, and S. R. Gunn, “Learning to Rank Images from Eye movements”, In: Proceeding of 2009 IEEE 12th International Conference on Computer Vision (ICCV'2009) Workshops on Human-Computer Interaction (HCI'2009), 27 Sep-4 Oct 2009, Kyoto, Japan, pp. 2009–2016, 2009.

9. D. R. Hardoon and K. Pasupa “Image Ranking with Implicit Feedback from Eye Movements,” In: Proceedings of the 6th Biennial Symposium on Eye Tracking Research & Applications (ETRA'2010), 22-24 Mar 2010, Austin, USA, pp. 291–-298, 2010.

10. P. Auer, Z. Hussain, S. Kaski, A. Klami, J. Kujala, J. Laaksonen, A. P. Leung, K. Pasupa, and J. Shawe-Taylor “Pinview: Implicit Feedback in Content-Based Image Retrieval, In: Proceeding of Workshop on Applications of Pattern Analysis (WAPA'2010), 1-2 Sep 2010, Cumberland Lodge, UK, pp 51–57, 2010.

11. C. E. Izard “Basic Emotions, Relations Among Emotions, and Emotion-Cognition Relations”, Psychological Review, 99(3), pp. 561–565, 1992.

12. K. Vytal and S. Hamann “Neuroimaging Support for Discrete Neural Correlates of Basic Emotions: A Voxel-based Meta-analysis,” Cognitive Neuroscience, 22(12), pp. 2864–2885, 2010.

13. P. Ekman “Universals and Cultural Differences in Facial Expressions of Emotion,” Nebraska Symposium on Motivation, 19, pp. 207-282, 1972.

14. R. E. Jack, O. G.B. Garrod, and P. G. Schyns “Dynamic Facial Expressions of Emotion Transmit an Evolving Hierarchy of Signals Over Time”, Current Biology, 24(2), pp. 187–192, 2014.

15. R. Plutchik and H. Kellerman, “Emotion: Theory, Research and Experience,” Psychological Medicine, 11(1), pp. 207, 1980.

16. D. W. Galenson “Two Paths to Abstract Art Kandinsky and Malevich”, Technical Report, National Bureau of Economic Research, No. 12403, 2006.

17. Y. Wu, C. Bauckhage and C. Thurau “The Good, the Bad, and the Ugly: Predicting Aesthetic Image Labels”, In: Proceedings of 20th International Conference on Pattern Recognition (ICPR’2010). 23-26 Aug 2010, Istanbul, Turkey. pp. 1586–1589, 2010.

18. The Eye Tribe Aps, The Eye Tribe, Available at https://theeyetribe.com.

19. P. Shaver, J., Schwartz, D., Kirson, C., O'Connor “Emotional Knowledge: Further Exploration of a Prototype Approach,” In: Emotions in Social Psychology: Essential Readings, pp. 26-56, 2001.

20. E. C. Chang, S., Mallat, C., Yap “Wavelet Foveation,” Appl. Comput. Harmon. Anal., 9, pp. 312-335, 2000