Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Factorization-Based Approach to Articulated Motion Recovery
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Articulated Structure from Motion by Factorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Motion segmentation with missing data using power factorization and GPCA
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
Incremental discovery of object parts in video sequences
Computer Vision and Image Understanding
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
International Journal of Computer Vision
Motion Segmentation from Feature Trajectories with Missing Data
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Segmentation of Rigid Motion from Non-rigid 2D Trajectories
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Task-Specific Functional Brain Geometry from Model Maps
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Learning Articulated Structure and Motion
International Journal of Computer Vision
Robust Algebraic Segmentation of Mixed Rigid-Body and Planar Motions from Two Views
International Journal of Computer Vision
International Journal of Computer Vision
Non-rigid metric reconstruction from perspective cameras
Image and Vision Computing
Enhanced model selection for motion segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
Enhanced Local Subspace Affinity for feature-based motion segmentation
Pattern Recognition
Visibility subspaces: uncalibrated photometric stereo with shadows
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Action recognition based on human movement characteristics
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Motion segmentation using the hadamard product and spectral clustering
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Rank Estimation in Missing Data Matrix Problems
Journal of Mathematical Imaging and Vision
Motion segmentation by model-based clustering of incomplete trajectories
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Stereoscopic Scene Flow Computation for 3D Motion Understanding
International Journal of Computer Vision
Mobile surveillance by 3D-outlier analysis
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Multi-body segmentation and motion number estimation via over-segmentation detection
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Recovering articulated non-rigid shapes, motions and kinematic chains from video
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Energy-Based Geometric Multi-model Fitting
International Journal of Computer Vision
3D motion segmentation using intensity trajectory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Local and structural consistency for multi-manifold clustering
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
A fast tri-factorization method for low-rank matrix recovery and completion
Pattern Recognition
A novel framework for motion segmentation and tracking by clustering incomplete trajectories
Computer Vision and Image Understanding
Hybrid Linear Modeling via Local Best-Fit Flats
International Journal of Computer Vision
Background subtraction using low rank and group sparsity constraints
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Soft inextensibility constraints for template-free non-rigid reconstruction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Robust and efficient subspace segmentation via least squares regression
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Semi-Nonnegative matrix factorization for motion segmentation with missing data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
SuperFloxels: a mid-level representation for video sequences
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Motion segmentation by velocity clustering with estimation of subspace dimension
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Background subtraction via coherent trajectory decomposition
Proceedings of the 21st ACM international conference on Multimedia
Editor's Choice Article: Motion-based segmentation of objects using overlapping temporal windows
Image and Vision Computing
A multi-manifold semi-supervised Gaussian mixture model for pattern classification
Pattern Recognition Letters
Greedy feature selection for subspace clustering
The Journal of Machine Learning Research
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We cast the problem of motion segmentation of feature trajectories as linear manifold finding problems and propose a general framework for motion segmentation under affine projections which utilizes two properties of trajectory data: geometric constraint and locality. The geometric constraint states that the trajectories of the same motion lie in a low dimensional linear manifold and different motions result in different linear manifolds; locality, by which we mean in a transformed space a data and its neighbors tend to lie in the same linear manifold, provides a cue for efficient estimation of these manifolds. Our algorithm estimates a number of linear manifolds, whose dimensions are unknown beforehand, and segment the trajectories accordingly. It first transforms and normalizes the trajectories; secondly, for each trajectory it estimates a local linear manifold through local sampling; then it derives the affinity matrix based on principal subspace angles between these estimated linear manifolds; at last, spectral clustering is applied to the matrix and gives the segmentation result. Our algorithm is general without restriction on the number of linear manifolds and without prior knowledge of the dimensions of the linear manifolds. We demonstrate in our experiments that it can segment a wide range of motions including independent, articulated, rigid, non-rigid, degenerate, non-degenerate or any combination of them. In some highly challenging cases where other state-of-the-art motion segmentation algorithms may fail, our algorithm gives expected results.