Abstract
The content-plot of a video clip is created by positioning several key frames in two-dimensions and connecting them with lines. It is constructed so that it should be possible to follow the events shown in the video by moving along the lines. Content plots were previously computed by clustering together frames that are contiguous in time. We propose to cluster together frames if they are related by a short chain of similarly looking frames even if they are not adjacent on the time-line. The computational problem can be formulated as a graph clustering problem that we solve by extending the classic k-means technique to graphs. This new graph clustering algorithm is the main technical contribution of this paper.
This material is based in part upon work supported by the Texas Advanced Research Program under Grant No. 009741-0074-1999 and the Texas Advanced Technology Program under Grant No. 009741-0042-2001
Chapter PDF
References
Xiong, W., Lee, J.C.M.: Efficient scene change detection and camera motion annotation for video classification. Computer Vision and Image Understanding: CVIU 71 (1998) 166–181
Zhang, H., Kankanhalli, A., Smoliar, S.: Automatic partitioning of full-motion video. ACM Multimedia Systems 1 (1993) 10–28
Yeung, M., Yeo, B., Liu, B.: Segmentation of video by clustering and graph analysis. Computer Vision and Image Understanding: CVIU 71 (1998) 94–109
Bolle, R.M., Yeo, B.L., Yeung, M.M.: Video query: Research directions. IBM Journal of Research and Development 42 (1998) 233–252
Dimitrova, N., Golshani, F.: Motion recovery for video content classification. ACM Transactions on Information Systems 13 (1995) 408–439
Yeo, B.L., Yeung, M.M.: Retrieving and visualizing video. Communications of the ACM 40 (1997) 43–52
Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to algorithms. 6th edn. MIT Press and McGraw-Hill Book Company (1992)
Schweitzer, H.: A distributed algorithm for content based indexing of images by projections on Ritz primary images. Data Mining and Knowledge Discovery 1 (1997) 375–390
Chandrasekaran, S., Manjunath, B.S., Wang, Y.F., Winkeler, J., Zhang, H.: An eigenspace update algorithm for image analysis. Graphical models and image processing: GMIP 59 (1997) 321–332
Horn, B.K., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17 (1981) 185–203
Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (1993) 1101–1113
Shi, J., Malik, J.: Normalized cuts and image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’97). (1997) 731–737
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Second edn. Academic Press, New York (1990)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, NJ (1988)
Marroquin, J.L., Girosi, F.: Some extensions of the K-means algorithm for image segmentation and pattern classification. Technical Report AIM-1390, Massachusetts Institute of Technology (1993)
Cox, T.F., Cox, M.A.: Multidimensional Scaling. Chapman & Hall (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schweitzer, H. (2002). Computing Content-Plots for Video. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47979-1_33
Download citation
DOI: https://doi.org/10.1007/3-540-47979-1_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43748-2
Online ISBN: 978-3-540-47979-6
eBook Packages: Springer Book Archive