Abstract
Recently, the rapid increase of the available amount of multimedia information has revealed an urgent need for developing intelligent methods for understanding and managing information. There are many features such as color, shape, texture and motion for semantic information in video data. Especially, the most important semantic information is based on a trajectory which is the significant factor for event representation of objects in video. In this paper, we focus on semantic representation using topological and directional relations between non-moving and moving objects. In the experiment part, we compared retrieval results using TSR(Tangent Space Representation) with those using rules represented by the proposed model. We extend queries and motion verbs in a specific domain (not general verbs) and apply the proposed method to an automatic annotation or narration system.
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Cho, M., et al.: Comparison between Motion Verbs using Similarity Measure for the Semantic Representation of Moving Object. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, pp. 282–290. Springer, Heidelberg (2006)
Aghbari, Z.A.: Studies on Modeling and Querying Video Databases, degree of Doctorate of Philosophy, Kyushu University (2001)
Egenhofer, M., Franzosa, R.: Point-set topological spatial relations. International Journal of Geographical Information Systems 5(2), 161–174 (1991)
Lee, S.Y., Hsu, F.J.: Spatial reasoning and similarity retrieval of images using 2D C-String knowledge representation. Pattern Recognition 25(3), 305–318 (1992)
Chang, J.W., Kim, Y.J., Chang, K.J.: A Spatial Match Representation Scheme for Indexing and Querying in Iconic Image Databases. In: Chang, J.W., Kim, Y.J., Chang, K.J. (eds.) ACM International Conference on Information and Knowledge Management, November 1997, pp. 169–176 (1997)
Chang, J.-W., Kim, Y.-J.: Spatial-Match Iconic Image Retrieval with Ranking in Multimedia Databases. In: Proceedings of Advances in Web-Age Information Management: Second International Conference (July 2001)
Ren, W., Singh, M., Singh, S.: Image Retrieval using Spatial Context. In: 9th International Workshop on Systems, Signals and Image Processing (November 2002)
Chen, P.-Y., Chen, A.L.P.: Video Retrieval Based on Video Motion Tracks of Moving Objects. In: Proceedings of SPIE, vol. 5307, pp. 550–558 (2003)
Levin, B.: English Verb Classes and Alternations: A preliminary Investigation. The University of Chicago Press (1993)
Baek, S., Hwang, M., Cho, M., Choi, C., Kim, P.: Object Retrieval by Query with Sensibility based on the Kansei-Vocabulary Scale. In: Huang, T.S., Sebe, N., Lew, M., Pavlović, V., Kölsch, M., Galata, A., Kisačanin, B. (eds.) ECCV 2006 Workshop on HCI. LNCS, vol. 3979, pp. 109–119. Springer, Heidelberg (2006)
Hwang, M., Baek, S., Kong, H., Shin, J., Kim, W., Kim, S., Kim, P.: Adaptive-Tangent Space Representation for Image Retrieval based on Kansei. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 828–837. Springer, Heidelberg (2006)
Li, J.Z., Ozsu, M.T., Szafron, D.: Modeling of Moving Objects in a Video Data-base. In: Proceedings of the International Conference on Multimedia Computing and Sys-tems, pp. 336–343 (1997)
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Cho, M., Choi, C., Choi, J., Yi, H., Kim, P. (2008). Trajectory Annotation and Retrieval Based on Semantics. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds) Adaptive Multimedia Retrieval: Retrieval, User, and Semantics. AMR 2007. Lecture Notes in Computer Science, vol 4918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79860-6_20
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DOI: https://doi.org/10.1007/978-3-540-79860-6_20
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