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.
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Notes
- 1.
This work is performed in the framework of European Community’s Seventh Framework Program (FP7/2007-2011) under grant agreement no. 216444 (PetaMedia) and the Swiss National Foundation for Scientific Research. The authors would also like to thank Krista Kappeler for her contribution on emotional characterization using MCA.
References
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)
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)
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)
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)
Bishop, C., en ligne), S.S.: Pattern Recognition and Machine Learning, vol. 4. springer, New York (2006)
Centeno, T., Lawrence, N.: Optimising kernel parameters and regularisation coefficients for non-linear discriminant analysis. J. Mach. Learn. Res. 7, 455–491 (2006)
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)
Cowie, R.: Emotion-Oriented Systems: The Humaine Handbook. Springer, Heidelberg (2010)
Ekman, P., Levenson, R., Friesen, W.: Autonomic nervous system activity distinguishes among emotions. Science 221(4616), 1208 (1983)
Fragopanagos, N., Taylor, J.: Emotion recognition in human-computer interaction. Neural Netw. 18(4), 389–405 (2005)
Hanjalic, A., Xu, L.: Affective video content representation and modeling. IEEE Trans. Multimed. 7(1), 143–154 (2005)
Healey, J.A.: Wearable and automotive systems for affect recognition from physiology. Ph.D. thesis, MIT (2000)
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)
Ishino, K., Hagiwara, M.: A feeling estimation system using a simple electroencephalograph. In: Proc. IEEE Int. Conf. Syst. Man Cybern. 5, 4204–4209 (2003)
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)
Kang, H.: Affective content detection using HMMs. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 259–262. ACM, New York (2003)
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)
Kim, J., André, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067–2083 (2008)
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)
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)
Kostyunina, M., Kulikov, M.: Frequency characteristics of EEG spectra in the emotions. Neurosci. Behav. Physiol. 26(4), 340–343 (1996)
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)
Lang, P., Greenwald, M., Bradeley, M., Hamm, A.: Looking at pictures- affective, facial, visceral, and behavioral reactions. Psychophysiology 30(3), 261–273 (1993)
Lartillot, O., Toiviainen, P., Eerola, T.: A matlab toolbox for music information retrieval. Data Analysis, Machine Learning and Applications, pp. 261–268 (2008)
Lee, J., Park, C.: Adaptive decision fusion for audio-visual speech recognition. Speech Recognition, Technologies and Applications, p. 550 (2008)
Lienhart, R.: Comparison of automatic shot boundary detection algorithms. Proc. SPIE 3656, 290–301 (1999)
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)
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)
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)
Mas, J., Fernandez, G.: Video shot boundary detection based on color histogram. In: Notebook Papers TRECVID2003, Gaithersburg, NIST (2003)
McFarland, R.: Relationship of skin temperature changes to the emotions accompanying music. Appl. Psychophysiol. Biofeedback 10(3), 255–267 (1985)
Pantic, M., Vinciarelli, A.: Implicit human-centered tagging [social sciences]. IEEE Signal Process. Mag. 26(6), 173–180 (2009)
Petrantonakis, P., Hadjileontiadis, L.: Emotion recognition from eeg using higher order crossings. IEEE Trans. Inf. Technol. Biomed. 14(2), 186–197 (2010)
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)
Plutchik, R.: The nature of emotions. Am. Sci. 89, 344 (2001)
Potamianos, G., Neti, C.: Stream confidence estimation for audio-visual speech recognition. In: Sixth International Conference on Spoken Language Processing (2000)
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)
Russell, J., Mehrabian, A.: Evidence for a three-factor theory of emotions. J. Res. Personal. 11(3), 273–294 (1977)
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)
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)
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)
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)
Ververidis, D., Kotropoulos, C.: Emotional speech recognition: resources, features, and methods. Speech Commun. 48(9), 1162–1181 (2006)
Wang, Y., Liu, Z., Huang, J.: Multimedia content analysis-using both audio and visual clues. Signal Process. Mag. IEEE 17(6), 12–36 (2000)
Yang, Y., Chen, H.: Ranking-based emotion recognition for music organization and retrieval. IEEE Trans. Audio Speech Lang. Process. 19(4), 762–774 (2011)
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.2008.52
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Yazdani, A., Lee, JS., Ebrahimi, T. (2013). Toward Emotional Annotation of Multimedia Contents. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_11
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