Abstract
This paper studies content-based video retrieval using the combination of audio and visual features. The visual feature is extracted by an adaptive video indexing technique that places a strong emphasis on accurate characterization of spatio-temporal information within video clips. Audio feature is extracted by a statistical time-frequency analysis method that applies Laplacian mixture models to wavelet coefficients. The proposed joint audio-visual retrieval framework is highly flexible and scalable, and can be effectively applied to various types of video databases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chang, Y.-L., Zeng, W., Kamel, I., Alonso, R.: Integrated image and speech analysis for content-based video indexing. In: Proc. of IEEE Int. Conf. on Multimedia Computing and Systems, pp. 306–313 (1996)
Dahyot, R., Kokaram, A., Rea, N., Denman, H.: Joint audio visual retrieval for tennis broadcasts. In: Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 3, pp. 561–564
Saraceno, C.: Video content extraction and representation using a joint audio and video processing. In: Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3033–3036 (1999)
Huang, J., Liu, Z., wang, Y., Chen, Y., Wong, E.K.: Integration of multimodal features for video scene classification based on HMM. In: IEEE Workshop on Multimedia Signal Processing, pp. 53–58 (1999)
Jasinschi, R.S., Dimitrova, N., McGee, T., Agnihotri, L., Zimmerman, J., Li, D., Louie, J.: A probabilistic layered framework fro integrating multimedia content and context information. In: Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 2, pp. 2057–2060 (2002)
Naphade, M.R., Huang, T.S.: Extracting semantics from audiovisual content: The final frontier in multimedia retrieval. IEEE Trans. on Neural Networks 13(4), 793–810 (2002)
Muneesawang, P., Guan, L.: Video retrieval using an adaptive video indexing technique and automatic relevance feedback. In: IEEE Workshop on Multimedia Signal Processing, pp. 220–223 (2003)
Kohonen, T.: Self-organising MAPS, 2nd edn. Springer, Heidelberg (1997)
Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions on Signal Processing 46(4), 886–902 (1998)
Wold, E., Blum, T., Keislar, D., Wheaton, J.: Content-based classificaiton, search and retrieval of audio. IEEE Multimedia 3(3), 27–36 (1996)
Saunders, J.: Real-Time Discrimination of Broadcast Speech /Music. In: IEEE Int. Conf. on Acoustic, Speech, and Signal Processing, Atlanta, vol. 2, pp. 993–996 (May 1996)
Bilmes, J.: A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report ICSI-TR-97-021, University of Berkeley (1998)
Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Tech. 8(5), 644–655 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muneesawang, P., Amin, T., Guan, L. (2004). Audio Visual Cues for Video Indexing and Retrieval. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30541-5_79
Download citation
DOI: https://doi.org/10.1007/978-3-540-30541-5_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23974-1
Online ISBN: 978-3-540-30541-5
eBook Packages: Computer ScienceComputer Science (R0)