Abstract
Organizing and visualizing video collections containing a high number of near duplicates is an important problem in film and video post-production. While kernels for matching sequences of feature vectors have been used e.g. for classification of video segments, kernel-based methods have not yet been applied to matching near duplicate video segments. In this paper we survey the application of six sequence-based kernels to clustering near duplicate video segments using kernel k-means and hierarchical clustering, and the application of kernel PCA for generating content visualizations for browsing. Evaluation on the TRECVID 2007 BBC rushes data set shows that the results of the kernel based methods are comparable to other approaches for matching near duplicates, eliminating differences between dynamic time warping and string matching. These results show that hierarchical clustering outperforms kernel k-means. We also show that well-arranged visualizations of both single- and multi-view content sets can be obtained using kernel PCA.
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
Bailer, W.: A Feature Sequence Kernel for Video Concept Classification. In: Lee, K.-T., Tsai, W.-H., Liao, H.-Y.M., Chen, T., Hsieh, J.-W., Tseng, C.-C. (eds.) MMM 2011 Part I. LNCS, vol. 6523, pp. 359–369. Springer, Heidelberg (2011)
Bailer, W., Lee, F., Thallinger, G.: A distance measure for repeated takes of one scene. The Visual Computer 25(1), 53–68 (2009)
Ballan, L., Bertini, M., Del Bimbo, A., Serra, G.: Video event classification using string kernels. Multimedia Tools Appl. 48(1), 69–87 (2010)
Choi, J., Jeon, W.J., Lee, S.-C.: Spatio-temporal pyramid matching for sports videos. In: Proc. 1st ACM International Conference on Multimedia Information Retrieval, pp. 291–297. ACM, New York (2008)
Cuturi, M., Vert, J.-P., Birkenes, O., Matsui, T.: A kernel for time series based on global alignments. Computing Research Repository, abs/cs/0610033 (2006)
Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: KDD, pp. 551–556 (2004)
Djordjevic, D., Izquierdo, E.: Relevance feedback for image retrieval in structured multi-feature spaces. In: Proc. MobiCom (2006)
Dumont, E., Mérialdo, B.: Rushes video parsing using video sequence alignment. In: Proc. CBMI 2009 (June 2009)
Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: IEEE ICCV, vol. 2 (2005)
Grauman, K., Darrell, T.: Approximate correspondences in high dimensions. In: NIPS, pp. 505–512 (2006)
Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. J. Mach. Learn. Res. 8, 725–760 (2007)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
Liu, Y., Zhou, F., Liu, W., De La Torre, F., Liu, Y.: Unsupervised summarization of rushes videos. In: Proc. ACM Multimedia, pp. 751–754 (2010)
Myers, C.S., Rabiner, L.R.: A comparative study of several dynamic time-warping algorithms for connected word recognition. The Bell System Technical Journal 60(7), 1389–1409 (1981)
NHK Science & Technical Research Laboratories. Test modules for TRECVID activity. Use case scenario. Ver.1.2.0E (April 2008)
Over, P., Smeaton, A.F., Awad, G.: The TRECVID 2008 BBC rushes summarization evaluation. In: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, TVS 2008, pp. 1–20. ACM, New York (2008)
Rahimi, A., Kiran, R.: How earth mover’s distance comprares two bags. Technical report, Intel Labs Berkeley (2007)
Ricci, E., Tobia, F., Zen, G.: Learning pedestrian trajectories with kernels. In: ICPR, pp. 149–152 (2010)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. of Computer Vision 40(2), 99–121 (2000)
Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5) (1998)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge Univ. Press (2004)
Shimodaira, H., Noma, K.-I., Nakai, M., Sagayama, S.: Dynamic time-alignment kernel in support vector machine. In: NIPS (2001)
Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: Proc. 8th ACM International Workshop on Multimedia Information Retrieval, pp. 321–330 (2006)
Xu, D., Chang, S.-F.: Visual event recognition in news video using kernel methods with multi-level temporal alignment. In: IEEE CVPR (2007)
Xu, D., Chang, S.-F.: Video event recognition using kernel methods with multilevel temporal alignment. IEEE Trans. Pattern Anal. Mach. Intell. 30 (2008)
Yeh, M.-C., Cheng, K.-T.: A string matching approach for visual retrieval and classification. In: Proc. 1st ACM International Conference on Multimedia Information Retrieval, pp. 52–58. ACM, New York (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bailer, W. (2012). Sequence Kernels for Clustering and Visualizing Near Duplicate Video Segments. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-27355-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27354-4
Online ISBN: 978-3-642-27355-1
eBook Packages: Computer ScienceComputer Science (R0)