Abstract
Emotion is the most abstract semantic structure of images. This paper overviews recent research on emotion semantics image retrieval. First, the paper introduces the general frame of emotion semantics image retrieval and points out the four main research issues: to exact sensitive features from images, to define users’ emotion information, to build emotion user model and to individualize the user model. Then several algorithms to solve these four issues are analyzed in detail. After that, some future research topics, including construction of an emotion database, evaluation of the user model and computation of the user model, are discussed, and some resolved strategies are presented elementarily.
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References
Amold, W.M.S., et al.: Content-Based image retrieval at the end of early years. IEEE Trans. On Pattern Analysis And Machine Intelligence 22(12), 1349–1380 (2000)
Zhao, R., William, I.: Bridging the semanitic gap in image retrieval. In: Distributed multimedia databases: techniques & applications, pp. 14–36. Idea Group Publishing, Hershey (2002)
Ozaki, K., Abe, S., Yano, Y.: Semantic retrieval on art museum database system. IEEE International Conference on Systems Man and Cybernetics 3, 2108–2112 (1996)
Bianchi-Berthouze, N., Kato, T.: K-DIME: An adaptive system to retrieval images from the WEB using subjective criteria. In: IEEE International Conference on System Man and Cybernetic 1999, Tokyo, Japan, vol. 6, pp. 358–362 (1999)
Wang, S.F.: Research on Emotion Information Processing and Its Application in Image Retrieval. In: Doctoral Dissertation, University of Science and Technology of China (May 2002)
Uemura, S., Arisawa, H., Arikawa, M., Kiyoki, Y.: Digital media information base. IEICE Transaction on Information and System E82-D(1), 22–33 (1999)
Arnheim, R.: Art and visual perception: A psychology of the creative eye. Regents of the University of California, Palo Alto (1954)
Itten, J.: Art of Color. Otto Maier, Ravensburg (1961)
Corridoni, M., Bimbo, A., Del Pala, P.: Retrieval of Paintings using Effects Induced by Color Features. In: 1998 International Workshop on Content-Based Access of Image and Video Databases, CAIVD 1998 (1998)
Mao, X., Chen, B., Muta, I.: Affective property of image and fractal dimension. Chaos Solitions and Fractals 13, 905–910 (2003)
Tanaka, S., Iwadate, Y., Inokuchi, S.: An attractiveness evaluation models based on the physical features of image regions. In: IEEE 15th International Conference on Pattern Recognition, Barcelona, Spain, pp. 793–796 (2000)
Kobayashi, Y., Kato, P.: Multi-contrast based texture model for understanding human subjectivity. In: 15th International Conference on Pattern Recognition, Barcelona, Spain, vol. 3, pp. 917–922 (2000)
Sung-Bae, C.: Emotional image and musical information retrieval with interactive genetic algorithm. Proceedings of the IEEE 92(4), 702–711 (2004)
Hayashi, T., Hagiwara, M.: An image retrieval system to estimate impression words from images using a neural network. In: IEEE International Conference on Systems Man and Cybernetics Computational Cybernetics and Simulation’, Orlando, FL, USA, vol. 1, pp. 150–155 (1997)
Shibata, T., Kato, T.: ”Kansei” image retrieval system for street landscape: discrimination and graphical parameters based on correlation of two images. In: IEEE International Conference on System Man and Cybernetic 1999, Tokyo, Japan, pp. 247–252 (1999)
Charles, E.O., Suci, G.J., Tannenbaum, P.H.: The measurement of meaning. University of Illinois Press, Urbana (1957)
Kobayashi, H., Ota, S.: The semantic network of KANSEI words. In: 2000 IEEE International Conference on Systems Man and Cybernetics, Nashville, TN, USA, vol. 1, pp. 690–694 (2000)
Sato, T., Kajiwara, K., Xin, J., Hansuebsai, A., Nobbs, J.: Numerical expression of colour emotion and its application
Yoshida, K., Kato, T., Yanaru, T.: Image retrieval system using impression words. In: 1998 IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 2780–2784 (1998)
Hayashi, T., Hagiwara, M.: Image query by impression words-the IQI system. IEEE Transactions on Consumer Electronics 44(2), 347–352 (1998)
Miura, K., Ozawa, J., Imanaka, T.: Information retrieval system based on a relative KANSEI model. In: Methodologies for the conception, Design and Application of Soft Computing, Proceedings of IIZUKA 1998, pp. 239–242 (1998)
Zukerman, I., Albrecht, D.W.: Predictive statistical models for user modeling. User Model and User-Adapted Interaction 11, 5–18 (2001)
Yoshida, K., Kato, T.: Database system for Kansei-oriented communication. Industrial Electronics Society. In: IECON 2000. 26th Annual Conference of the IEEE, Nagoya, Japan, vol. 3, pp. 1604–1607 (2000)
Chin, D.N.: Empirical Evaluation of user models and user-adapted systems. User Model and User-Adapted Interaction 11, 181–194 (2001)
Lotfi, A.Z.: Applied Soft computing-foreword. Applied soft computing, 1–2 (2001)
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Wang, S., Wang, X. (2005). Emotion Semantics Image Retrieval: An Brief Overview. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_63
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DOI: https://doi.org/10.1007/11573548_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
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