Abstract
In this paper we propose the Time Interval Multimedia Event (TIME) framework as a robust approach for recognition of multimedia patterns, e.g. highlight events, in soccer video. The representation used in TIME extends the Allen temporal interval relations and allows for proper inclusion of context and synchronization of the heterogeneous information sources involved in multimedia pattern recognition. For automatic classification of highlights in soccer video, we compare three different machine learning techniques, i.c. C4.5 decision tree, Maximum Entropy, and Support Vector Machine. It was found that by using the TIME framework the amount of video a user has to watch in order to see almost all highlights can be reduced considerably, especially in combination with a Support Vector Machine.
This research is sponsored by the ICES/KIS MIA project and TNO-TPD.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
AIELLO, M. et al. (2002): Document understanding for a broad class of documents. Intʻl Journal on Document Analysis and Recognition, 5/1, 1–16.
ALLEN. J.F. (1983): Maintaining knowledge about temporal intervals. Communications of the ACM 26/11, 832–843.
ASSFALG, J. et al. (2002): Soccer highlights detection and recognition using HMMs. In: IEEE Intʻl Conf. on Multimedia & Expo, Lausanne, Switzerland.
BAAN, J. et al. (2001): Lazy users and automatic video retrieval tools in (the) lowlands. In: Proc. of the 10th Text REtrieval Conf. Gaithersburg, USA.
BABAGUCHI, N. et al. (2002): Event based indexing of broadcasted sports video by intermodal collaboration. IEEE Trans. on Multimedia, 4/1, 68–75.
BERGER, A. et al. (1996): A maximum entropy approach to natural language processing. Computational Linguistics, 22/1, 39–71.
DARROCH, J.N. and RATCLIFF, D. (1972): Generalized iterative scaling for loglinear models. The Annals of Mathematical Statistics, 43/5, 1470–1480.
EKIN, A. et al. (2003): Automatic soccer video analysis and summarization. IEEE Trans. on Image Processing, 12/7, 796–807.
FISCHER, S. et al. (1995): Automatic recognition of film genres. In: ACM Multimedia, San Francisco, USA, 295–304.
HAN, M. et al. (2002): An integrated baseball digest system using maximum entropy method. In: ACM Multimedia, Juan-les-Pins, France.
JAIN, A.K. et al. (2000): Statistical pattern recognition: A review. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22/1, 4–37.
JAYNES, E.T. (1957): Information theory and statistical mechanics. The Physical Review, 106/4, 620–630.
LAU, R. et al. (1993): Adaptive language modelling using the maximum entropy approach. In: ARPA Human Language Technologies Workshop Princeton, USA, 81–86.
LIN, W.-H. and HAUPTMANN, A.G. (2002): News video classification using SVMbased multimodal classifiers and combination strategies. In: ACM Multimedia, Juan-les-Pins, France.
NAPHADE, M.R. and HUANG, T.S. (2001): A probabilistic framework for semantic video indexing, filtering, and retrieval. IEEE Trans. on Multimedia, 3/1, 141–151.
QUINLAN, J.R. (1993): C4.5: Programs for Machine Learning. Morgan Kaufmann.
ROWLEY, H.A. et al. (1998): Neural network-based face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20/1, 23–38.
SMEULDERS, A.W.M. et al. (2000): Content based image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22/12, 1349–1380.
SNOEK, C.G.M. and WORRING, M. (2003): Time interval maximum entropy based event indexing in soccer video. In IEEE Intʻl Conf. on Multimedia & Expo, volume 3, Baltimore, USA, 481–484.
SNOEK, C.G.M. and WORRING, M. (2005): Multimodal video indexing: A review of the state-of-the-art. Multimedia Tools and Applications. In press.
VAPNIK, V.N. (2000): The Nature of Statistical Learning Theory, 2th ed. Springer, New York.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Snoek, C.G., Worring, M. (2005). Multimedia Pattern Recognition in Soccer Video Using Time Intervals. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_9
Download citation
DOI: https://doi.org/10.1007/3-540-28084-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
Online ISBN: 978-3-540-28084-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)