Social Media Retrieval pp 239-259

Part of the Computer Communications and Networks book series (CCN)

Toward Emotional Annotation of Multimedia Contents

Chapter

Abstract

By annotating multimedia contents, users of a web resource can associate a word or a phrase (tag) with that resource such that other users can retrieve it by means of searching. Nowadays, tags play an important role in search and retrieval process in multimedia content sharing social networks. Explicit tagging refers to assigning tags directly in an explicit way such as typing. Implicit tagging, however, refers to assigning tags by observing users’ behaviors during exposure to multimedia contents. Among various kinds of information that can be obtained for the purpose of implicit tagging, emotional information about a given content is of great interest. In this chapter, we discuss various means of emotion recognition and emotional characterization, which can be used as tools for emotional tagging. A P300-based brain-computer interface system is proposed for the purpose of emotional tagging of multimedia content. We show that this system can successfully perform emotional tagging and naive users who have not participated in the training of the system can also use it efficiently. Furthermore, we present emotional annotating systems using multimedia content analysis and electroencephalogram signal processing and will compare them. Finally, a road map for developing a practical multimodal system for implicit emotional annotation of multimedia contents will be sketched out.

References

  1. 1.
    Abd-Almageed, W.: Online, simultaneous shot boundary detection and key frame extraction for sports videos using rank tracing. In: 15th IEEE International Conference on Image Processing, 2008. ICIP 2008, pp. 3200–3203. IEEE, Piscataway (2008)Google Scholar
  2. 2.
    Adams, W., Iyengar, G., Lin, C., Naphade, M., Neti, C., Nock, H., Smith, J.: Semantic indexing of multimedia content using visual, audio, and text cues. EURASIP J. Appl. Signal Process. 2, 170–185 (2003)Google Scholar
  3. 3.
    Aftanas, L., Reva, N., Varlamov, A., Pavlov, S., Makhnev, V.: Analysis of evoked EEG synchronization and desynchronization in conditions of emotional activation in humans: temporal and topographic characteristics. Neurosci. Behav. Physiol. 34(8), 859–867 (2004)CrossRefGoogle Scholar
  4. 4.
    Ames, M., Naaman, M.: Why we tag: motivations for annotation in mobile and online media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 971–980. ACM, New York (2007)Google Scholar
  5. 5.
    Bishop, C., en ligne), S.S.: Pattern Recognition and Machine Learning, vol. 4. springer, New York (2006)Google Scholar
  6. 6.
    Centeno, T., Lawrence, N.: Optimising kernel parameters and regularisation coefficients for non-linear discriminant analysis. J. Mach. Learn. Res. 7, 455–491 (2006)MathSciNetMATHGoogle Scholar
  7. 7.
    Chanel, G., Kronegg, J., Grandjean, D., Pun, T.: Emotion assessment: arousal evaluation using EEG’s and peripheral physiological signals. Multimedia Content Representation, Classification and Security, pp. 530–537. Springer, Berlin/New York (2006)Google Scholar
  8. 8.
    Cowie, R.: Emotion-Oriented Systems: The Humaine Handbook. Springer, Heidelberg (2010)Google Scholar
  9. 9.
    Ekman, P., Levenson, R., Friesen, W.: Autonomic nervous system activity distinguishes among emotions. Science 221(4616), 1208 (1983)CrossRefGoogle Scholar
  10. 10.
    Fragopanagos, N., Taylor, J.: Emotion recognition in human-computer interaction. Neural Netw. 18(4), 389–405 (2005)CrossRefGoogle Scholar
  11. 11.
    Hanjalic, A., Xu, L.: Affective video content representation and modeling. IEEE Trans. Multimed. 7(1), 143–154 (2005)CrossRefGoogle Scholar
  12. 12.
    Healey, J.A.: Wearable and automotive systems for affect recognition from physiology. Ph.D. thesis, MIT (2000)Google Scholar
  13. 13.
    Hoffmann, U., Vesin, J., Ebrahimi, T., Diserens, K.: An efficient p300-based brain-computer interface for disabled subjects. J. Neurosci. methods 167(1), 115–125 (2008)CrossRefGoogle Scholar
  14. 14.
    Ishino, K., Hagiwara, M.: A feeling estimation system using a simple electroencephalograph. In: Proc. IEEE Int. Conf. Syst. Man Cybern. 5, 4204–4209 (2003)Google Scholar
  15. 15.
    Joho, H., Jose, J., Valenti, R., Sebe, N.: Exploiting facial expressions for affective video summarisation. In: Proceedings of the ACM International Conference on Image and Video Retrieval, p. 31. ACM, New York (2009)Google Scholar
  16. 16.
    Kang, H.: Affective content detection using HMMs. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 259–262. ACM, New York (2003)Google Scholar
  17. 17.
    Kierkels, J., Soleymani, M., Pun, T.: Queries and tags in affect-based multimedia retrieval. In: IEEE International Conference on Multimedia and Expo, 2009. ICME 2009, pp. 1436–1439. IEEE (2009)Google Scholar
  18. 18.
    Kim, J., André, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067–2083 (2008)CrossRefGoogle Scholar
  19. 19.
    Kim, K., Bang, S., Kim, S.: Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Comput. 42(3), 419–427 (2004)CrossRefGoogle Scholar
  20. 20.
    Koelstra, S., Muhl, C., Soleymani, M., Lee, J., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: Deap: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 99, 1–1 (2011)Google Scholar
  21. 21.
    Kostyunina, M., Kulikov, M.: Frequency characteristics of EEG spectra in the emotions. Neurosci. Behav. Physiol. 26(4), 340–343 (1996)CrossRefGoogle Scholar
  22. 22.
    Krause, C., Viemerö, V., Rosenqvist, A., Sillanmäki, L., Åström, T.: Relative electroencephalographic desynchronization and synchronization in humans to emotional film content: an analysis of the 4–6, 6–8, 8–10 and 10–12 Hz frequency bands. Neurosci. Lett. 286(1), 9–12 (2000)CrossRefGoogle Scholar
  23. 23.
    Lang, P., Greenwald, M., Bradeley, M., Hamm, A.: Looking at pictures- affective, facial, visceral, and behavioral reactions. Psychophysiology 30(3), 261–273 (1993)CrossRefGoogle Scholar
  24. 24.
    Lartillot, O., Toiviainen, P., Eerola, T.: A matlab toolbox for music information retrieval. Data Analysis, Machine Learning and Applications, pp. 261–268 (2008)Google Scholar
  25. 25.
    Lee, J., Park, C.: Adaptive decision fusion for audio-visual speech recognition. Speech Recognition, Technologies and Applications, p. 550 (2008)Google Scholar
  26. 26.
    Lienhart, R.: Comparison of automatic shot boundary detection algorithms. Proc. SPIE 3656, 290–301 (1999)CrossRefGoogle Scholar
  27. 27.
    Lin, Y., Wang, C., Jung, T., Wu, T., Jeng, S., Duann, J., Chen, J.: Eeg-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798–1806 (2010)CrossRefGoogle Scholar
  28. 28.
    Lisetti, C.L., Nasoz, F.: Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP J. Appl. Signal Process. 2004(1), 1672–1687 (2004)CrossRefGoogle Scholar
  29. 29.
    Lopatovska, I., Arapakis, I.: Theories, methods and current research on emotions in library and information science, information retrieval and human–computer interaction. Inf. Process. Manag. 47(4), 575–592 (2011)CrossRefGoogle Scholar
  30. 30.
    Mas, J., Fernandez, G.: Video shot boundary detection based on color histogram. In: Notebook Papers TRECVID2003, Gaithersburg, NIST (2003)Google Scholar
  31. 31.
    McFarland, R.: Relationship of skin temperature changes to the emotions accompanying music. Appl. Psychophysiol. Biofeedback 10(3), 255–267 (1985)Google Scholar
  32. 32.
    Pantic, M., Vinciarelli, A.: Implicit human-centered tagging [social sciences]. IEEE Signal Process. Mag. 26(6), 173–180 (2009)CrossRefGoogle Scholar
  33. 33.
    Petrantonakis, P., Hadjileontiadis, L.: Emotion recognition from eeg using higher order crossings. IEEE Trans. Inf. Technol. Biomed. 14(2), 186–197 (2010)CrossRefGoogle Scholar
  34. 34.
    Picard, R., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1175–1191 (2001)CrossRefGoogle Scholar
  35. 35.
    Plutchik, R.: The nature of emotions. Am. Sci. 89, 344 (2001)Google Scholar
  36. 36.
    Potamianos, G., Neti, C.: Stream confidence estimation for audio-visual speech recognition. In: Sixth International Conference on Spoken Language Processing (2000)Google Scholar
  37. 37.
    Rasheed, Z., Sheikh, Y., Shah, M.: On the use of computable features for film classification. IEEE Trans. Circuits Sys. Video Technol. 15(1), 52–64 (2005)CrossRefGoogle Scholar
  38. 38.
    Russell, J., Mehrabian, A.: Evidence for a three-factor theory of emotions. J. Res. Personal. 11(3), 273–294 (1977)CrossRefGoogle Scholar
  39. 39.
    Schaaff, K., Schultz, T.: Towards emotion recognition from electroencephalographic signals. In: Proceedings of International Conference on Affective Computing and Intelligent Interaction and Workshops, Amsterdam, pp. 1–6 (2009)Google Scholar
  40. 40.
    Sebe, N., Cohen, I., Gevers, T., Huang, T.: Emotion recognition based on joint visual and audio cues. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006, vol. 1, pp. 1136–1139. IEEE, Washington, DC (2006)Google Scholar
  41. 41.
    Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multi-modal affective database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 99, 1–1 (2011)Google Scholar
  42. 42.
    Sun, K., Yu, J.: Video affective content representation and recognition using video affective tree and hidden markov models. Affective Computing and Intelligent Interaction, pp. 594–605. Springer, Berlin/New York (2007)Google Scholar
  43. 43.
    Ververidis, D., Kotropoulos, C.: Emotional speech recognition: resources, features, and methods. Speech Commun. 48(9), 1162–1181 (2006)CrossRefGoogle Scholar
  44. 44.
    Wang, Y., Liu, Z., Huang, J.: Multimedia content analysis-using both audio and visual clues. Signal Process. Mag. IEEE 17(6), 12–36 (2000)CrossRefGoogle Scholar
  45. 45.
    Yang, Y., Chen, H.: Ranking-based emotion recognition for music organization and retrieval. IEEE Trans. Audio Speech Lang. Process. 19(4), 762–774 (2011)CrossRefGoogle Scholar
  46. 46.
    Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009). doi:10.1109/TPAMI.2008.52CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Ashkan Yazdani
    • 1
  • Jong-Seok Lee
    • 2
  • Touradj Ebrahimi
    • 1
  1. 1.Multimedia Signal Processing Group (MMSPG)École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  2. 2.School of Integrated TechnologyYonsei UniversityIncheonKorea

Personalised recommendations