Abstract
Manually labeling video data is not only a labor intensive and time-consuming task, but also subject to human errors. In this paper, we present an automatic video annotation system. The system uses spatial attributions such as color, texture, shape, motion, and temporal hierarchical attributes among video objects. The system includes a new method of automatic video segmentation, object recognition and object-tracking scheme, and hierarchical object-based video representation model.
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References
Chang, S.-F., Chen, W., Meng, H., Sundaram, H., Zhong, D.: A fully automated content based video search engine supporting spatio-temporal queries. IEEE Transaction on Circuits and Systems for Video Technology 8(5), 602–615 (1998)
Chauhan, A., Singh, S., Grosvenor, D.: Episode detection in videos captured using a head mounted camera. Pattern Analysis and Applications 7, 176–189 (2004)
Comaniciu, D., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 25(5), 564–576 (2003)
Comaniciu, D., Meer, P.: Mean Shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 1–18 (2002)
Deng, Y., Manjunath, B.S.: NeTra-V: towards an object-based video representation. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 616–627 (1998)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3, 610–621 (1973)
Li, J.Z., Zsu, M.T., Szafron, D.: Modeling video temporal relationships in an object database management system. In: Proc. SPIE Multimedia Computing and Networking (MMCN 1997), pp. 80–91 (1997)
Mindru, F., Moons, T., Van Gool, L.: Recognizing color patterns irrespective of viewpoint and illumination. In: Proc. CVPR 1999, Fort Collins, Colorado, pp. 368–373 (1999)
O’Connor, N., Sav, S., Adamek, T., Mezaris, V., Kompatsiaris, I., Lui, T.Y., Izquierdo, E., Bennstrom, C.F., Casas, J.R.: Region and Object Segmentation Algorithms in the QIMERA Segmentation Platform. In: CBMI 2003 International Workshop on Content-Based Multimedia Indexing, Rennes, France, September 2003, pp. 22–24 (2003)
Oomoto, E., Tanaka, K.: Ovid: Design and implementation of video-object database system. IEEE Trans. On Knowledge and Data Engineering 5(4), 629–643 (1994)
Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. Computer Vision and Pattern Recognition, 193–199 (1999)
Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994)
Scott, D.W.: Multivariate Density Estimation. Wiley, Chichester (1992)
Smith, T.G.A., Davenport, G.: The stratification system: a design environment for random access video. In: Workshop on Network and operating system, La Jolla, CA (1992)
Weiss, R., Duda, A., Gifford, D.K.: Content-based access to algebraic video. In: Proc. IEEE First International Conference on Multimedia Computing and Systems, May 1994, pp. 140–151 (1994)
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© 2005 Springer-Verlag Berlin Heidelberg
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Ren, W., Singh, S. (2005). An Automated Video Annotation System. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_76
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DOI: https://doi.org/10.1007/11552499_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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