Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A novel multi-resolution video representation scheme based on kernel PCA
The Visual Computer: International Journal of Computer Graphics
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
SIFT Flow: Dense Correspondence across Different Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
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Video summarization is an efficient and flexible way to represent video data. In this paper, we use the Kernel PCA and clustering based key frame extraction to realize multilevel video representation. In order to remove the redundancy caused by large scene changes, SIFT flow scene alignment is performed on the clustering set of key frames. After alignment, one representative frame is chosen from the reconstructed cluster set on matched frame pairs. We explore the difference on data structures between frame level and scene level, and modify the FCM method on the cluster number initialization for video summarization. Experimental results are presented to verify the efficiency of our approach.