Abstract
This paper proposes a new key frame extraction method by using color spatial distribution histograms. Pixels in the same color are contiguous or non-contiguous to each other in an image, which is a noticeable spatial character. With this property, the pixels are classified as contiguous and non-contiguous ones. Therefore, refined histograms distinguish the small changes better than the original histograms between frames, and it allows fine distinctions that are similar to original histograms. With the evaluation of our experiments, this approach extract key frames more efficiently and accurately.
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References
Lee, H.-C., Kim, S.-D.: Iterative key frame selection in the rate-constraint environment. Signal Processing: Image Communication 18, 1–15 (2003)
Hanjalic, A., Zhang, H.: An integrated scheme for automated video abstraction based on unsupervised cluster validity analysis. IEEE Transaction on Circuits and System for Video Technology 9 (1999)
Lee, S., Hayes, M.H.: An application for interactive video abstraction. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 905–908 (2004)
Ferman, A.M., Tekalp, A.M.: Two-stage hierarchical video summary extraction to match low-level user browsing preferences. IEEE Transactions on Multimedia 5, 244–256 (2003)
Feng, H., Fang, W., Liu, S., Fang, Y.: A new general framework for shot boundary detection and key-frame extraction. In: The 7th ACM SIGMM international workshop on Multimedia information retrieval, Hilton, Singapore, pp. 121–126 (2005)
Ho, Y.-H., Chen, W.-R., Lin, C.-W.: A rate-constrained key-frame extraction scheme for channel-aware video streaming. In: Proceedings of IEEE International Conference on Image Processing, Singapore, pp. 613–616 (2004)
Porter, S.V., Mirmehdi, M., Thomas, B.T.: A shortest path representation for video summarization. In: Proceedings of the 12th International Conference on Image Analysis and Processing, Mantova, Italy, pp. 460–465 (2003)
Li, Z., Schuster, G., Katsaggelos, A.K., Gandhi, B.: Optimal video summarization with a bit budget constraint. In: Proceedings of IEEE International Conference on Image Processing, Singapore, pp. 613–616 (2004)
Liu, T., Zhang, H.J., Qi, F.: A novel video key-frame-extraction algorithm based on perceived motion energy model. IEEE Trans. Circuits Syst. Video Technol. 13, 1006–1013 (2003)
Ouyang, J.-q., Li, J.-t., Tang, H.: Interactive key frame selection model. Journal of Visual Communication and Image Representation 17, 1145–1163 (2006)
Arman, F., Hsu, A., Chiu, M.-Y.: Image processing on compressed data for large video databases. In: 1st ACM International Conference on Multimedia, Anaheim, CA, USA, pp. 267–272. ACM, New York (1993)
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Chang, J., Hu, R., Wang, Z., Hang, B. (2009). Extracting Key Frames for Surveillance Video Based on Color Spatial Distribution Histograms. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_96
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DOI: https://doi.org/10.1007/978-3-642-10467-1_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10466-4
Online ISBN: 978-3-642-10467-1
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