Synonyms
Image/Video/Music search; Multimedia information retrieval
Definition
Semantic modeling and knowledge representation is essential to a multimedia information retrieval system for supporting effective data organization and search. Semantic modeling and knowledge representation for multimedia data (e.g., imagery, video, and music) consists of three steps: feature extraction, semantic labeling, and features-to-semantics mapping. Feature extraction obtains perceptual characteristics such as color, shape, texture, salient-object, and motion features from multimedia data; semantic labeling associates multimedia data with cognitive concepts; and features-to-semantics mapping constructs correspondence between perceptual features and cognitive concepts. Analogically to data representation for text documents, improving semantic modeling and knowledge representation for multimedia data leads to enhanced data organization and query performance.
Historical Background
The principal design...
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Aizerman MA, Braverman EM, Rozonoer LI. Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control. 1964;25:821–37.
Barnard K, Forsyth D. Learning the semantics of words and pictures. Int Conf Comput Vision. 2000;2:408–15.
Beyer K, Goldstein J, Ramakrishnan R, Shaft U. When is nearest neighbor meaningful. In: Proceedings of the 7th International Conference on Database Theory; 1999. p. 217–35.
Blei DM, Ng A, Jordan M. Latent Dirichlet allocation. J Machine Learning Res. 2003;3(4/5):993–1022.
Chang EY, et al. Parallelizing support vector machines on distributed computers. In: Advances in Neural Information Proceedings of the Systems 20, Proceedings of the 21st Annual Conference on Neural Information Proceedings of the Systems; 2007.
Datta R, Joshi D, Li J, Wang JZ. Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv. 2008;40(65).
Donoho DL. Aide-Memoire. High-dimensional data analysis: the curses and blessings of dimensionality (American Math. Society Lecture). In: Mathematical Challenges Of The 21st Century Explored at American Mathematical Society Conference; 2000.
Flickner M, et al. Query by image and video content: QBIC system. IEEE Comput. 1995;28(9).
Goh K, Chang EY, Lai W-C. Concept-dependent multimodal active learning for image retrieval. In: Proceedings of the 12th ACM International Conference on Multimedia; 2004. p. 564–71.
Hoi C-H, Lyu MR. A novel log-based relevance feedback technique in content-based image retrieval. In: Proceedings of the 12th ACM International Conference on Multimedia; 2004. p. 24–31.
Li B, Chang EY. Discovery of a perceptual distance function for measuring image similarity. ACM Multimedia Syst J. (Special Issue on Content-Based Image Retrieval). 2003;8(6):512–22.
Rocchio JJ. Relevance feedback in information retrieval. In: Salton G, editor. The SMART retrieval system – experiments in automatic document processing. Englewood Cliffs: Prentice-Hall; 1971. p. 313–23. Chapter 14.
Rui Y, Huang TS, Chang S-F. Image retrieval: current techniques, promising directions and open issues. J Visual Commn Image Represent. 1999;10(1):39–62.
Tong S, Chang EY. Support vector machine active learning for image retrieval. In: Proceedings of the 9th ACM International Conference on Multimedia; 2001. p. 107–18.
Tversky A. Features of similarity. Psychol Rev. 1997;84(4):327–52.
Wu G, Chang EY. KBA: Kernel Boundary Alignment considering imbalanced data distribution. IEEE Trans Knowl Data Eng. 2005;17(6):786–95.
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Chang, E.Y. (2018). Semantic Modeling and Knowledge Representation for Multimedia Data. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1038
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DOI: https://doi.org/10.1007/978-1-4614-8265-9_1038
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