Multilevel video representation with application to keyframe extraction

Content-based video analysis calls for efficient video representation. In this paper, a novel multi-level representation of video is proposed based on the principle components derived from low-level visual features. It can characterize the video content from the coarse level to the fine level according to its intrinsic structure. This representation form provides a flexible scheme for video content analysis such as summarization, classification, and retrieval. A newly proposed subspace method, kernel based PCA, is explored to achieve this conveniently. The application in keyframe extraction is investigated to demonstrate the benefits of this representation.

[1]  Avideh Zakhor,et al.  Content analysis of video using principal components , 1998, IEEE Trans. Circuits Syst. Video Technol..

[2]  Arif Ghafoor,et al.  Spatio-temporal modeling of video data for on-line object-oriented query processing , 1995, Proceedings of the International Conference on Multimedia Computing and Systems.

[3]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[4]  Boon-Lock Yeo.  On fast microscopic browsing of MPEG-compressed video , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[5]  Sang Uk Lee,et al.  Efficient video indexing scheme for content-based retrieval , 1999, IEEE Trans. Circuits Syst. Video Technol..

[6]  Shingo Uchihashi,et al.  Video Manga: generating semantically meaningful video summaries , 1999, MULTIMEDIA '99.

[7]  A. Murat Tekalp,et al.  Two-stage hierarchical video summary extraction to match low-level user browsing preferences , 2003, IEEE Trans. Multim..

[8]  Dragutin Petkovic,et al.  Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review , 1996 .

[9]  Anthony A. Maciejewski,et al.  Eigen-decomposition-based analysis of video images , 1998, Electronic Imaging.

[10]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[11]  Kwang In Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Processing Letters.

[12]  Xin Liu,et al.  Video summarization using singular value decomposition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Arif Ghafoor,et al.  A multi-level abstraction and modeling in video databases , 1999, Multimedia Systems.

[14]  Alan Hanjalic,et al.  An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis , 1999, IEEE Trans. Circuits Syst. Video Technol..

[15]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[16]  Christos Faloutsos,et al.  Developing high-level representations of video clips using VideoTrails , 1997, Electronic Imaging.

[17]  Chong-Wah Ngo,et al.  On clustering and retrieval of video shots through temporal slices analysis , 2002, IEEE Trans. Multim..