A Fuzzy-Based Multimedia Content Retrieval Method Using Mood Tags and Their Synonyms in Social Networks.
DOI:
https://doi.org/10.9781/ijimai.2022.10.005Keywords:
Cost-Satisfaction, Fuzzy, Multimedia, Social NetworkAbstract
The preferences of Web information purchasers are rapidly evolving. Cost-effectiveness is now becoming less regarded than cost-satisfaction, which emphasizes the purchaser’s psychological satisfaction. One method to improve a user’s cost-satisfaction in multimedia content retrieval is to utilize the mood inherent in multimedia items. An example of applications using this method is SNS (Social Network Services), which is based on folksonomy, but its applications encounter problems due to synonyms. In order to solve the problem of synonyms in our previous study, the mood of multimedia content is represented with arousal and valence (AV) in Thayer’s two-dimensional model as its internal tag. Although some problems of synonyms could now be solved, the retrieval performance of the previous study was less than that of a keyword-based method. In this paper, a new method that can solve the synonym problem is proposed, while simultaneously maintaining the same performance as the keyword-based approach. In the proposed method, a mood of multimedia content is represented with a fuzzy set of 12 moods of the Thayer model. For the analysis, the proposed method is compared with two methods, one based on AV value and the other based on keyword. The analysis results demonstrate that the proposed method is superior to the two methods.
Downloads
References
C. B. Moon, J. Y. Yi, D.-S. Kim, B. M. Kim, “Analysis of Overlapping Mood Tags Based on Synonyms,” Korea Computer Congress 2018 (KCC 2018), KIISE (2018), June 20-22; ICC JEJU, Korea, pp.667-669, 2018, doi: 10.9723/JKSIIS.2019.24.1.013.
Russel, J. A., “A Circumplex Model of Affect,” Journal of Personality and Social Psychology, Vol.39, No.6, pp.1161-1178, 1980, doi: 10.1037/h0077714.
Hevner, K., “Experimental studies of the elements of expression in music,” The American Journal of Psychology, Vol.48, No.2, pp.246-268, 1936, doi: 10.2307/1415746.
Thayer, R. E., The Biopsychology of Mood and Arousal, Oxford University Press, 1990.
C. B. Moon, H. S. Kim, B. M. Kim, “Music Retrieval Method using Mood Tag and Music AV Tag based on Folksonomy,” Journal of KIISE, Vol.40, No.9, pp.526-543, 2013, doi: 10.9723/jksiis.2019.24.1.013.
C. B. Moon, H.S. Kim, H. A. Lee, B. M. Kim, “Analysis of Relationships Between Mood and Color for Different Musical Preferences,” Color Research & Application, Vol.39, No.4, pp.413-423, 2014, doi: 10.1002/col.21806.
C. B. Moon, H.S. Kim, D. W. Lee, B. M. Kim, “Mood Lighting System Reflecting Music Mood,” COLOR research and application, Vol.40, No.2, pp.201-212, 2015, doi: 10.1002/col.21864.
Viskovich, Shelley Narelle, Evaluation of a web-based Acceptance and Commitment Therapy (ACT) program to increase self-care and mental health skills in university students, PhD Thesis, School of Psychology, The University of Queensland, 2019.
Klinenberg, E., Going solo: the extraordinary rise and surprising appeal of living alone, London: Penguin Books, 2012.
C. B. Moon, J. Y. Lee, D.-S. Kim, B. M. Kim, “Multimedia Content Recommendation in Social Networks using Mood Tags and Synonyms,” Multimedia Systems, 26, pp.139–156, 2020, doi: 10.1007/s00530-019-00632-w.
W.-J. Wang, “New similarity measures on fuzzy sets and on elements,” Fuzzy sets and systems, vol. 85, pp. 305–309, 1997, doi: 10.1016/0165-0114(95)00365-7.
D.-G. Wang, Y.-P. Meng, and H.-X. Li, “A fuzzy similarity inference method for fuzzy reasoning,” Computers and mathematics with applications, vol. 56, pp. 2445–2454, 2008, doi: 10.1016/j.camwa.2008.03.054.
C. P. Pappis and I. Karacapilidis, “A comparative assessment of measures of similarity of fuzzy values,” Fuzzy sets and systems, vol. 56, pp. 171–174, 1993, doi: 10.1016/0165-0114(93)90141-4.
L. Baccour, A. M. Alimi, R. I. John, “Some notes on fuzzy similarity measures and application to classification of shapes recognition of arabic sentences and mosaic,” IAENG International Journal of Computer Science, vol. 41, no. 2, pp. 81-90, 2014.
Huang, W., Wu, Q., Dey, N., Ashour, A., Fong, S. J., & González-Crespo, R., “Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 10, pp. 28-37, 2020, doi: 10.9781/ijimai.2020.05.002.
Ness, S. R., Theocharis, A., Tzanetakis, G. and Martins, L. G., “Improving automatic music tag annotation using stacked generalization of probabilistic svm outputs,” Proc. of the 17th ACM international conference on Multimedia, pp.705-708, 2009, doi: 10.1145/1631272.1631393.
Laurier, C., Sordo, M., Serra, J. and Herrera, P., “Music mood representations from social tags,” Proc. of the 10th International Society for Music Information Conference, Kobe, Japan, pp.381-386, 2009.
Kim, J., Lee, S., Kim, S. and Yoo, W. Y., “Music mood classification model based on arousal-valence values,” Proc. of 13th International Conference on Advanced Communication Technology (ICACT), 2011, pp.292-295, 2011.
A. T. Ji, et al., “Collaborative Tagging in Recommender Systems,” AI 2007: Advances in Artificial Intelligence, pp. 377–386, 2007, doi: 10.1007/978-3-540-76928-6_39.
K. H. L. Tso-Sutter, et al., “Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms,” SAC ‘08: Proceedings of the 2008 ACM symposium on Applied computing, pp. 1995–1999, 2008, doi: 10.1145/1363686.1364171.
M. Vojnovic, et al., “Ranking and Suggesting Popular Items,” IEEE Transactions on Knowledge and Data Engineering, vol.21, pp.1133-1146, 2009, doi: 10.1109/TKDE.2009.34.
S. Yang, S. Kim and Y. M. Ro, “Semantic Home Photo Categorization,” IEEE Trans. Circuits and Systems for Video Technology, Vol.17, No.3, March 2007, pp.324-335, 2007, doi: 10.1109/TCSVT.2007.890829.
S. Yang, S. K. Kim, K. S. Seo, Y. M. Ro, J. Kim and Y. S. Seo, “Semantic categorization of digital home photo using photographic region templates,” International Journal of Information Processing and Management, Vol.43, No.2, March 2007, pp.503-514, 2007, doi: 10.1016/j.ipm.2006.07.009.
S. Yang, R. M. Ro, “Photo Indexing Using Person-Based Multi-feature Fusion with Temporal Context,” Proc. International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, Nov. 2007, pp.257-262, 2007, doi: 10.1109/UBICOMM.2007.29.
E. Chang, G. Kingshy, G. Sychay, G. Wu, “CBSA: Content-Based Soft Annotation for Multimodal Image Retrieval using Bayes Point Machines,” IEEE Trans. Circuits and Systems for Video Technology, Vol.13, No.1, January 2003, pp.26–38, 2003, doi: 10.1109/TCSVT.2002.808079.
J. Li and J. Z. Wang, “Real-Time Computerized Annotation of Pictures,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.30, No.6, June 2008, pp.985-1002, 2008, doi: 10.1109/TPAMI.2007.70847.
R. M ö rzinger, R. Sorschag, G. Thallinger and S. Lindstaedt, “Automatic Image Annotation using Visual Content and Folksonomies,” Multimedia Tools and Applications, Vol.42, No.1, March 2009, pp.97-113, 2009, doi: 10.1007/s11042-008-0247-7.
S.h. Rho, B.-J. Han, and E. Hwang, “A fuzzy inference-based music emotion recognition system,” In Proc. Visual Information Engineering, pp. 673–677, 2008, doi: 10.1049/cp:20080398.
S. Qamar, P. Ahmad, “Emotion Detection from Text using Fuzzy Logic,” International Journal of Computer Applications, Vol. 121, No. 3, pp. 29-32, 2015, doi: 10.5120/21522-4501.
Magdin, M., Držík, D., Reichel, J., & Koprda, S., “The Possibilities of Classification of Emotional States Based on User Behavioral Characteristics,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 4, pp. 97-104, 2020, doi: 10.9781/ijimai.2020.11.010
D. Powers, “Evaluation: From precision recall and f-measure to roc informedness markedness and correlation,” J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2011, doi: 10.9735/2229-3981.
https://drive.google.com/file/d/18KmHezs55_fMNj95XgceIDYOhhxu3HmZ/view
Downloads
Published
-
Abstract184
-
PDF20
Submit Article
Information
