Video Feature Extraction Based on Improved Locality Preserving Projections
XIAO Yong-Liang1,2,XIA Li-Min1
1.School of Information Science and Engineering,Central South University,Changsha 410083 2.Department of Information Management,Hunan College of Finance and Economics,Changsha 410205
Abstract：A method to extract video feature is introduced. To solve the problems related to the projection dimension and nearest neighbor K in locality preserving projections (LPP), the method to determine the optimal projection dimension based on structure error between dimension reduction before and after is proposed in this papers. The nearest neighbor K is dynamically selected combining with the neighbor statistical character of each data. On the basis of the above an optimal projection matrix of video feature is obtained by using LPP, and then the high dimension feature of new video is reduced to a lower one through the projection matrix. The comparison of experimental results show that the feature based on LPP is more favorable for shot segmentation than the other features.
[1] Camara-Chavez G, Precioso F, Cord M, et al. Shot Boundary Detection by a Hierarchical Supervised Approach // Proc of the 14th International Conference on Systems, Signals and Image Processing. Maribor, Slovenia, 2007: 197-200 [2] Vasconcelos N, Lippman A. Statistical Models of Video Structure for Content Analysis and Characterization. IEEE Trans on Image Processing, 2000, 9(1): 3-19 [3] Hanjalic A. Shot-Boundary Detection: Unraveled and Resolved. IEEE Trans on Circuits and Systems for Video Technology, 2002, 12(2): 90-105 [4] Truong B T, Venkatesh S. Video Abstraction: A Systematic Review and Classification. ACM Trans on Multimedia Computing, Communications and Applications, 2007, 3(1): 1-37 [5] Yuan Jinhui, Wang Huiyi, Xiao Lan, et al. A Formal Study of Shot Boundary Detection. IEEE Trans on Circuits and Systems for Video Technology, 2007, 17(2): 168-186 [6] Bellman R. Adaptive Control Processes: A Guided Tour. Princeton, USA: Princeton University Press, 1961 [7] Tenenbaum J B, Silva V D, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality. Science, 2000, 290(5500): 2319-2323 [8] Saul L K, Roweis S T. An Introduction to Locally Linear Embedding [EB/OL]. [2009-03-27]. http://www.cs.toronto.edu/~roweis/lle/papers/lleintro.pdf [9] Belkin M, Niyogi P. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering // Proc of the Conference on Neural Information Processing Systems. Vancouver, Canada, 2002: 585-591 [10] He Xiaofei, Niyogi P. Locality Preserving Projections // Proc of the Conference on Neural Information Processing Systems. Vancouver, Canada, 2003: 153-160 [11] Shawn M, Alex B. Estimating Manifold Dimension by Inversion Error // Proc of the ACM Symposium on Applied Computing. Santa Fe, USA, 2005: 22-26 [12] Wen Guihua, Jiang Lijun, Wen Jun. Dynamically Determining Neighborhood Parameter for Locally Linear Embedding, Journal of Software, 2008, 19(7): 1666-1673 (in Chinese) (文贵华,江丽君,文 军.邻域参数动态变化的局部线性嵌入.软件学报, 2008, 19(7): 1666-1673) [13] Cooper M, Liu T, Rieffel E. Video Segmentation via Temporal Pattern Classification. IEEE Trans on Multimedia. 2007, 9(3): 610-618
2010, Vol. 23 
