Abstract
In this paper we propose an innovative combination strategy for a system using video and audio stream of a news video to automatically segment it into stories. In our approach, the segmentation is performed in two steps: first, shots are classified by combining three different anchor shot detection algorithms using video information only. Then, the shot classification is improved by using a novel anchor shot detection method based on features extracted from the audio track.
Experimental results demonstrate that the combined use of audio and video allows our system to perform better than approaches based only on video information in terms of both shot classification and news story segmentation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hanjalic, A., Lagendijk, R.L., Biemond, J.: Semi-Automatic News Analysis, Indexing, and Classification System Based on Topics Preselection. In: Proc. of SPIE: Electronic Imaging: Storage and Retrieval of Image and Video Databases, San Jose, CA (1999)
Gao, X., Tang, X.: Unsupervised Video-Shot Segmentation and Model-Free Anchorperson Detection for News Video Story Parsing. IEEE Transactions on Circuits and Systems for Video Technology 12(9), 765–776 (2002)
Bertini, M., Del Bimbo, A., Pala, P.: Content-based indexing and retrieval of TV News. Pattern Recognition Letters 22, 503–516 (2001)
Wei, W., Gao, W.: Automatic Segmentation of News Items Based on Video and Audio Features. Journal of Computer Science and Technology 17(2), 189–195 (2002)
De Santo, M., Percannella, G., Sansone, C., Vento, M.: Combining experts for anchorperson shot detection in news videos. Pattern Analysis and Applications 7(4) (2004) (in press – online first: DOI: 10.1007/s10044-004-0227-0)
De Santo, M., Percannella, G., Sansone, C., Vento, M.: A Multi-Expert Approach for Shot Classification in News Videos. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 564–571. Springer, Heidelberg (2004)
Huang, Y.S., Suen, C.Y.: A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(1), 90–94 (1995)
Sansone, C., Tortorella, F., Vento, M.: A Classification Reliability Driven Reject Rule for Multi-Expert Systems. International Journal of Pattern Recognition and Artificial Intelligence 15(6), 885–904 (2001)
Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A Real-Time Text-Independent Speaker Identification System. In: Proceedings of the 12th International Conference on Image Analysis and Processing, Mantova, September 17-19, pp. 632–637 (2003)
Xu, L., Krzyzak, A., Suen, C.Y.: Methods of Combining Multiple Classifiers and Their Application to Handwritten Numeral Recognition. IEEE Trans. on Systems, Man and Cybernetics 22(3), 418–435 (1992)
Xu, L., Krzyzak, A., Oja, E.: Rival Penalized Competitive Learning for Clustering Analysis, RBF net and Curve Detection. IEEE Trans. on Neural Networks 4, 636–649 (1993)
Gargi, U., Kasturi, R., Strayer, S.H.: Performance Characterization of Video-Shot-Change Detection Methods. IEEE Trans. on Circuits and Systems for Video Technology 10(1), 1–13 (2000)
Chaisorn, L., Chua, T.-S., Lee, C.-H.: A Multi-Modal Approach to Story Segmentation for News Video. World Wide Web 6, 187–208 (2003)
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
De Santo, M., Percannella, G., Sansone, C., Vento, M. (2005). Combining Audio-Based and Video-Based Shot Classification Systems for News Videos Segmentation. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_40
Download citation
DOI: https://doi.org/10.1007/11494683_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
eBook Packages: Computer ScienceComputer Science (R0)