Abstract
Accessing and organizing home videos present technical challenges due to their unrestricted content and lack of storyline. In this paper, we propose a spectral method to group video shots into scenes based on their visual similarity and temporal relations. Spectral methods have been shown to be effective in capturing perceptual organization features. In particular, we investigate the problem of automatic model selection, which is currently an open research issue for spectral methods, and propose measures to assess the validity of a grouping result. The methodology is used to group scenes from a six-hour home video database, and is assessed with respect to a ground-truth generated by multiple people. The results indicate the validity of the proposed approach, both compared to existing techniques as well as the human ground-truth.
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References
F.R.K. Chung, Spectral Graph Theory, American Mathematical Society, 1997.
D. Comaniciu, V. Ramesh, and P. Meer, “Real-Time Tracking of Non-Rigid Objects using Mean Shift,” in Proc. IEEE CVPR., Hilton Head Island, S. C., June 2000.
D. Gatica-Perez, A. Loui, and M.T. Sun, “Finding Structure in Home Videos by Probabilistic Hierarchical Clustering,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 13, No. 5, Jun. 2003.
G. Iyengar and A. Lippman, “Content-based browsing and edition of unstructured video,” in Proc. IEEE ICME, New York City, Aug. 2000.
J.R. Kender and B. L. Yeo, “On the Structure and Analysis of Home Videos,” in Proc. ACCV, Taipei, Jan. 2000.
S. Vempala R. Kannan and A. Vetta, “On clusterings-good, bad and spectral,” in Proc. 41st Symposium on the Foundation of Computer Science, FOCS, 2000.
A. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: analysis and an algorithm,” in Proc. NIPS, Vancouver, Dec 2001.
J.-M. Odobez, D. Gatica-Perez and M. Guillemot, “On Spectral Methods and Structuring of Home Videos,” IDIAP Technical Report, IDIAP-RR-55, Nov. 2002.
J. Platt “AutoAlbum: Clustering Digital Photographs using Probablisitic Model Merging,” in Proc. IEEE Workshop on CBAIVL, Hilton Head Island, S. C.,2000.
A. Savakis, S. Etz, and A. Loui, “Evaluation of image appeal in consumer photography,” in Proc. SPIE Conf. on Human Vision and EI, Jan. 2000.
G. L. Scott and H.C. Longuet-Higgins, “Feature grouping by relocalisation of eigenvectors of the proximity matrix,” in Proc. BMVC, 1990, pp. 103–108.
J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000.
M. Yeung, B. L. Yeo, and B. Liu, “Segmentation of Video by Clustering and Graph Analysis,” Comp. Vision and Image Underst., Vol. 71, No. 1, pp. 94–109, July 1998.
Y. Weiss, “Segmentation using eigenvectors: a unifying view,” in Proc. ICCV, 1999.
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© 2003 Springer-Verlag Berlin Heidelberg
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Odobez, JM., Gatica-Perez, D., Guillemot, M. (2003). Spectral Structuring of Home Videos. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_31
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DOI: https://doi.org/10.1007/3-540-45113-7_31
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