Abstract
In a ubiquitous environment, video transfer is very important. In particular, transferring of selected video clips obtained through scene change detection is more important than transferring an entire video. In this paper, inter-frame difference values are first computed through combining the χ 2 histogram with the color histogram, as well as normalization. Next, key frames for a cluster are determined through distance clustering and K-mean clustering. Lastly, key frames for a group are determined through a likelihood ratio. According to our experiments, the proposed method surpassed other methods in its ability to detect scene changes due to the use of three steps: difference value calculation, clustering, and key frame extraction.
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
Antani, S., Kasturi, R., Jain, R.: A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recognition 35(4), 945–965 (2002)
Ferman, A.M., Tekalp, A.M., Mehrotra, R.: Robust Color Histogram Descriptors for Video Segment Retrieval and Identification. IEEE Transactions on Image Processing 11(5), 497–508 (2002)
Lee, M.S., Yang, Y.M., Lee, S.W.: Automatic video parsing using shot boundary detection and camera operation analysis. Pattern Recognition 34(3), 711–719 (2001)
Yeung, M., Yeo, B., Liu, B.: Segmentation of Video by Clustering and Graph Analysis. Computer Vision and Image Understanding 71(1), 94–109 (1998)
Yeo, B.-L.: Efficiency processing of compressed image and video. Technical report, PhD thesis, Princeton University (1996)
Zhang, H., Low, C.Y., Gong, Y., Smoliar, S.: Video Parsing Using Compressed Data. In: Proc. SPIE Conf. Image and Video Processing II, vol. 2182, pp. 142–149 (1994)
Zhong, D., Chang, S.F.: Video Object Model and Segmentation for Content-Based Video Indexing. In: IEEE International Conference on Circuits and Systems (June 1997)
Yeung, M.M., Yeo, B.-L., Wolf, W., Liu, B.: Video Browsing Using Clustering and Scene Transition Compressed Sequences. IS&T SPIE, Multimedia Computing and Networking (1995)
Zhang, H., Tan, S.Y., Smoliar, S.W., Yihong, G.: Automatic Parsing and Indexing of News Video. Multimedia Systems 2, 256–266 (1995)
Shin, S.Y., Pyo, S.B.: Video Browsing Using An Efficient Scene Change Detection in Telematics. Journal of KSCI 11(4), 147–154 (2006)
Kim, Y.L., Rhee, Y.W.: Scene Change Detection Using Local χ 2-Test. Journal of KSCI 11(3), 193–202 (2006)
Sarah, V.P.: Video Segmentation and indexing using Motion Estimation. Doctorial Dissertation, University of Bristol (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shin, SY., Lee, JH., Park, SJ., Lee, JC., Pyo, SB., Rhee, YW. (2009). Phased Scene Change Detection in Ubiquitous Environments. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02454-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-02454-2_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02453-5
Online ISBN: 978-3-642-02454-2
eBook Packages: Computer ScienceComputer Science (R0)