Algorithms for clustering data
Algorithms for clustering data
Clustering algorithms based on minimum and maximum spanning trees
SCG '88 Proceedings of the fourth annual symposium on Computational geometry
Handbook of pattern recognition & computer vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
ACM Computing Surveys (CSUR)
Self-Organizing Maps
International Journal of Computer Vision
Deterministic Texture Analysis and Synthesis Using Tree Structure Vector Quantization
SIBGRAPI '99 Proceedings of the XII Brazilian Symposium on Computer Graphics and Image Processing
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction of Temporal Texture Based on Spatiotemporal Motion Trajectory
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Building k Edge-Disjoint Spanning Trees of Minimum Total Length for Isometric Data Embedding
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Minimum Spanning Tree Based Clustering Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes
International Journal of Computer Vision
Rushes summarization with self-organizing maps
Proceedings of the international workshop on TRECVID video summarization
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
From still image to video-based face recognition: an experimental analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Dynamic texture recognition using normal flow and texture regularity
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Synergizing spatial and temporal texture
IEEE Transactions on Image Processing
Fourier spectral factor model for prediction of multidimensional signals
Signal Processing
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
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Dynamic textures play an important role in video content analysis. Current works of dynamic textures mainly focus on overall texture and motion analysis for segmentation or classification based on statistical features and structure models. This paper proposes a novel framework to study the dynamic textures by exploring the motion trajectory using unsupervised learning. A nonlinear dimensionality reduction algorithm, called hybrid distance isometric embedding (HDIE), is proposed, to generate a low-dimensional motion trajectory from high-dimensional feature space of the raw video data. First, we partition the high-dimensional data points into a set of data clusters and construct the intra-cluster graphs based on the individual character of each data cluster to build the basic layer of HDIE. Second, we construct the inter-cluster graph by analyzing the interrelation among these isolated data clusters to build the top layer of HDIE. Finally, we generate a whole graph and map all data points into a unique low-dimensional feature space, trying to maintain the distances of all pairs of high-dimensional data points. Experiments on the standard dynamic texture database show that the proposed framework with the novel algorithm can represent the motion characters of the dynamic textures very well.