Abstract A dualchannel extension of nonlinear dimensionality reduction algorithm is proposed according to the characteristic of stereo video sequence. In order to construct a mapping between high and low dimensional spaces, kneighbor kernel function method is presented, which solves the problem of lack of reconstruction algorithm and provides a new solution to video compression. Experimental results on several typical sequences show the advantages of the proposed approaches.
LI Hua,DU SiDan,LU Fan等. Feature Extraction and Image Reconstruction of Video Sequence Based on Nonlinear Dimensionality Reduction Algorithms[J]. , 2006, 19(5): 645-651.
[1] Seung H S, Lee D D. The Manifold Ways of Perception. Science, 2000, 290(5500): 2268-2269 [2] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326 [3] Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323 [4] Pless R. Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences // Proc of the 9th IEEE International Conference on Computer Vision. Nice, France, 2003, Ⅱ: 1433-1440 [5] Jenkins O C, Mataric M J. A Spatio-Temporal Extension to Isomap Nonlinear Dimension Reduction // Proc of the 21st International Conferrence on Machine Learing. Banff, Canada, 2004: 441-448 [6] de Juan C, Bodenheimer B. Cartoon Textures // Proc of the Eurographics/ACM SIGGRAPH Symposium on Computer Animation. Grenoble, France, 2004: 267-276 [7] Zhang J P, Li S Z, Wang J. Manifold Learning and Applications in Recognition // Tan Y P, Yap K H, Wang L, eds. Intelligent Multimedia Processing with Soft Computing. Heidelberg, Germany: Springer-Verlag, 2005: 281-300 [8] Poggio T, Girosi F. Networks for Approximation and Learning. Proc of the IEEE, 1990, 78(9): 1481-1497 [9] Elgammal A, Lee C S. Inferring 3D Body Pose from Silhouettes Using Activity Manifold Learning // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA, 2004, Ⅱ: 681-688 [10] Kouropteva O, Okun O, Pietikainen M. Selection of the Optimal Parameter Value for the Locally Linear Embedding Algorithm // Proc of the 1st International Conference on Fuzzy Systems and Knowledge Discovery. Singapore, Singapore, 2002: 359-363 [11] Bengio Y, Paiement J F, Vincent P. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps and Spectral Clustering // Thrun S, Saul I K, Schlkopf B, eds. Advances in Neural Information Processing Systems 16. Cambridge, USA: MIT Press, 2004: 177-184