Vision and Around

Dedicated to my research and life

Subspace Segmentation

Geometric modeling of structured data with low-dimensional subspaces/manifolds, with applications in signal processing, robust control, and computational vision (segmentation). (Update: Nov 03 2014)

2014

  1. Subspace clustering of dimensionality-reduced data
  2. Learning Subspaces of Different Dimension
  3. Robust Subspace Segmentation with Block-diagonal Prior (CVPR)
  4. Smooth Representation Clustering (CVPR)
  5. Greedy Subspace Clustering (NIPS)

2013

  1. Greedy feature selection for subspace clustering (Manuscript)
  2. Robust subspace clustering (Manuscript)
  3. Noisy sparse subspace clustering (ICML)
  4. Subspace clustering via thresholding and spectral clustering (ICCASP)
  5. Noisy subspace clustering via thresholding (ISIT)
  6. Robust subspace clustering via thresholding (Manuscript, full version of the immediately above two papers)
  7. A new approach to two-view motion  segmentation using global dimension minimization (Manuscript)
  8. Discriminative subspace clustering (CVPR)
  9. Scalable sparse subspace clustering (CVPR)
  10. Correlation Adaptive Subspace Segmentation by Trace Lasso (ICCV)
  11. Correntropy Induced L2 Graph for Robust Subspace Clustering (ICCV)
  12. Robust Subspace Clustering via Half-Quadratic Minimization (ICCV)
  13. Minimal Basis Facility Location for Subspace Segmentation (ICCV)
  14. Distributed Low-Rank Subspace Segmentation (ICCV)
  15. Latent Space Sparse Subspace Clustering (ICCV)
  16. Provable Subspace Clustering: When LRR meets SSC (NIPS)
  17. Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering (NIPS)

2012

  1. Improved subspace clustering via exploitation of spatial constraints (CVPR)
  2. Higher order motion models and spectral clustering (CVPR)
  3. Robust and efficient subspace segmentation via least square regressions (ECCV)
  4. Probabilistic low-rank subspace clustering (NIPS)
  5. Group-wise constrained reconstruction for subspace clustering (ICML)
  6. Sparse subspace clustering: algorithms, theory, and applications (PAMI)
  7. Constructing L2-Graph For Subspace Learning and Segmentation (Manuscript)

2011

  1. Latent low-rank representation for subspace segmentation and feature extraction (ICCV)
  2. A Closed Form Solution to Robust Subspace Estimation and Clustering (CVPR)
  3. Graph Connectivity in Sparse Subspace Clustering (CVPR)
  4. Generalized Projection Based M-Estimator: Theory and Applications (CVPR)
  5. A Global Optimization Approach to Robust Multi-Model Fitting (CVPR)
  6. A Geometric Analysis of Subspace Clustering with Outliers (Manuscript, accepted by Annals of Statistics 2012)
  7. High-rank matrix completion and subspace clustering with missing data (Manuscript)

2010

  1. Robust Low-Rank Subspace Segmentation with Semi-Definite Guarantees(ICDM)
  2. Robust Recovery of Subspace Structures by Low-Rank Representation (Preprint)
  3. Robust subspace segmentation by low-rank representation (ICML)
  4. Group Motion Segmentation Using a Spatio-Temporal Driving Force Model (CVPR, not exactly)
  5. Clustering Disjoint Subspaces via Sparse Representation (ICASSP)
  6. Object Segmentation by Long Term Analysis of Point Trajectories (ECCV. Not quite subspace segmentation, albeit motion segmentation)
  7. Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories (PAMI)
  8. A Tutorial on Subspace Clustering (Signal Processing Magazine)
  9. GPCA with Denoising: A Moments-Based Convex Approach (CVPR)
  10. Probabilistic Recovery of Multiple Subspaces in Point Clouds by Geometric Minimization (Manuscript)

2009

  1. Spectral clustering of linear subspaces for motion segmentation (ICCV)
  2. Non-Negative Matrix Factorization of Partial Track Data for Motion Segmentation (ICCV)
  3. The Normalized Subspace Inclusion: Robust Clustering of Motion Subspaces (ICCV)
  4. Sparse subspace clustering (CVPR)
  5. Spectral Curvature Clustering (IJCV)

2008

  1. Motion Segmentation via Robust Subspace Separation in the Presence of Outlying, Incomplete, or Corrupted Trajectories (CVPR, Agglomerative Lossy Compression (ALC) for segmentation and sparse representation for noise handling)
  2. Clustering and Dimensionality Reduction on Riemannian Manifolds(CVPR)
  3. Subspace Segmentation with Outliers: A Grassmannian Approach to the Maxim um Consensus Subspace (CVPR)

2007

  1. Two-View Motion Segmentation by Mixtures of Dirichlet Process with Model Selection and Outlier Removal (ICCV)
  2. A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms (CVPR)
  3. Two-view Motion Segmentation from Linear Programming Relaxation (CVPR)
  4. Projective Factorization of Multiple Rigid-Body Motions (CVPR,  alternating between depth recovery and motion grouping under perspective projection)
  5. Segmenting Motions of Different Types by Unsupervised Manifold Clustering (CVPR, LLE Manifold dimensionality reduction + clustering to deal with generic motions)
  6. Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data (SIAM Review)

2006

  1. Incorporating non-motion cues into 3D motion segmentation (ECCV)
  2. A General Framework for Motion Segmentation: Independent, Articulated, Rigid, Non-rigid, Degenerate and Non-degenerate(ECCV)
  3. Nonrigid Shape and Motion from Multiple Perspective Views (ECCV)
  4. Online clustering of moving hyperplanes (NIPS)

2005

  1. Generalized Principal Component Analysis (PAMI)

2004

  1. The Multibody Trifocal Tensor: Motion Segmentation from 3 Perspective Views (CVPR, direct extension)
  2. Motion Segmentation with Missing Data using PowerFactorization and GPCA (CVPR, multiframe 3D affine motion segmentation. Power Projection + GPCA + Spectral Clustering. Handle degenerate cases also)
  3. A Unified Algebraic Approach to 2D and 3D Motion Segmentation (ECCV, GPCA with two-view or optic flow)
  4. Multibody factorization with uncertainty and missing data using the EM algorithm (CVPR )

2003

  1. Generalized Principal Component Analysis (CVPR. Estimating a mixture of linear subspaces from sampled data points. Later applied to multiframe 3D motion segmentation )
  2. Optimal Segmentation of Dynamic Scenes from Two Perspective Views (CVPR. Refined two-view multibody SfM bypassing segmentation and optimized for noisy cases)
  3. Degeneracies, dependencies and their implications in multi-body and multi-sequence factorization (CVPR)

2002

  1. A Factorization Method for 3D Multi-body Motion Estimation and Segmentation (Annual Allerton Conference on Communication, Control and Computing. Factorization based on the subspace constraint introduced by M. Irani to infinitesimal image measurement to 3D segmentation under perspective projection )
  2. Segmentation of Dynamic Scenes from the Multibody Fundamental Matrix (ECCV workshop on vision and modeling of dynamic scenes. Two-view mutlibody SfM under full perspective projection, introducing the multibody epipolar constraint and the multibody fundamental matrix)

2001

  1. Two-body segmentation from two perspective views (CVPR)
  2. Multibody grouping via orthogonal subspace decomposition (CVPR)
  3. Motion segmentation by subspace separation and model selection (ICCV)

Classical References before 2001

  1. A multibody factorization method for independently moving objects (IJCV 1998. Multibody segmentation under orthographic projection)
  2. Factorization-based segmentation of motions (IEEE workshop on motion understanding 1991)

Disclaimer - This page is meant to serve a hub for references on this problem, and does not represent in any way personal endorsement of papers listed here. So I do not hold any responsibility for quality and technical correctness of each paper listed here. The reader is advised to use this resource with discretion.

If you’d like your paper to be listed here - Just drop me a few lines via email (which can be found on “Welcome” page). If you don’t bother to spend a word, just deposit your paper on arXiv. I get email alert about new animals there every morning,  and will be happy to hunt one for this zoo if it seems fit.

Comments