3D fuzzy GIST to analyze emotional features in movies

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7435 LNCS, Issue , 2012, Pages 192-199

  Kwon, Mingu ^a   Lee, Minho ^a  

^a Kyungpook National University   (South Korea)

Author keywords

3D fuzzy GIST; adaptive neurofuzzy inference classifier; positive and negative emotion

Indexed keywords

3D FUZZY GIST; ADAPTIVE NEURO-FUZZY INFERENCE; DYNAMIC FEATURES; FUZZY APPROACH; GIST FEATURES; MEAN OPINION SCORES; ORIENTATION INFORMATION; POSITIVE AND NEGATIVE EMOTION;

MOTION PICTURES; THREE DIMENSIONAL;

FUZZY INFERENCE;

EID: 84865029273     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32639-4_24     Document Type: Conference Paper

References (23)

1. Colombo, C., Bimbo, A., Pala, P.: Semantics in visual information retrieval. IEEE Multimedia 6(3), 38-53 (1999)
2. Iwadatae, Y., Inoue, M., Suzuki, R., Hikawa, N., Makino, M., Kanemoto, Y.: MIC Interatcive Dance System. In: Proceedings of the International Conference on Knowledge-based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 95-98 (2000)
3. Assfalg, J., Bertini, M., Colombo, C., Bimbo, A.: Semantic annotation of sports videos. IEEE Multimedia 9(2), 52-60 (2002)
4. Yu, C., Xu, L.: An emotion-based approach to decision making and self learning in autonomous robot control. In: The Fifth World Congress on Intelligent Control and Automation, vol. 3, pp. 2386-2390 (2004)
5. Oatley, K., Jenkins, J.: Understanding Emotions. Blackwell Publishers, Cambridge (1996)
6. Günenkin, B., Basar, E.: Emotion face expressions are differentiated with brain oscillations. International Journal of Psychophysiology 64, 91-100 (2007)
7. Hasson, U., Malach, R., Heeger, D.J.: Reliablility of cortical activity during natural stimulation. Trends in Cognitive Sciences 14(1), 40-48 (2010)
8. Zhang, Q., Lee, M.: Emotion development system by interacting with human EEG and natural scene understanding. Cognitive System Research 14, 37-49 (2012)
9. Zhang, Q., Jeong, S., Lee, M.: Autonomous emotion development using incremental modified adaptive neuro-fuzzy inference system. Neurocomputing 86, 33-44 (2012)
10. Picard, R.: Affective computing. MIT Press, London (1997)
11. Weining, W., Yinglin, Y., Jianchao, Z.: A new SVM based emotional classification of image. Journal of Electronics 22(1), 98-104 (2005)
12. Wang, W.N., Yu, Y.L., Jiang, S.M.: Image retrieval by emotional semantics: A study of emotional space and feature extraction. In: IEEE International Conference on Proceedings Systems, Man and Cybernetics, vol. 4, pp. 3534-3539 (2006)
13. Feichtinger, H.G., Strohmer, T.: Gabor Analysis and Algorithms. Birkhäuser (1998)
14. CIE, Commission Internationale de TEclairageColorimetry. Publication No. 15. Bureau central de la, Vienna, Austria (1976)
15. Choi, J.-H., Sohn, J.-I., Yang, H.-S., Chung, Y.-R., Lee, M., Koh, S.-C.: A study of colour emotion and colour preference. part I: Colour emotions for single colours. Color Research and Application 29(3), 232-240 (2004)
16. Itten, J.: Art of Color. John Wiley & Sons, Inc., New York (1961)
17. Zhang, Q., Lee, M.: Fuzzy-GIST for 4-Emotion Recognition in Natural Scene Images. In: DEVLRN 2009: Proceedings of the 2009 IEEE 8th International Conference on Development and Learning, pp. 1-7. IEEE Computer Society, Washingon, DC (2009)
18. Zhang, Q., Lee, M.: Analysis of positive and negative emotions in natural scene using brain activity and gist. Neurocomputing 72(4-6), 1302-1306 (2009)
19. Oliva, A., Torralba, A.: Building the gist of a scene: the role of global image features in recognition. Progress in Brain Research 155, 23-36 (2006)
20. Bezdek, J.: Fuzzy mathematics in pattern classification. Ph.D. thesis, Cornell University Ithaca (1973)
21. Dubois, D., Prade, H.: Possibility theory is not fully compositional!: a comment on a short note by Greenberg, H.J. Fuzzy Sets Syst. 95(1), 131-134 (1998)
22. Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Inc., Upper Saddle River (1997)
23. Castellano, G.: A neurofuzzy methodology for predictive modeling. Ph.D. dissertation, Univ. of Bari (2000)