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Low-rank models in visual analysis : theories, algorithms, and applications /
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press, an imprint of Elsevier,
[2017]
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| Colección: | Computer vision and pattern recognition series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 1 Introduction; References; 2 Linear Models; 2.1 Single Subspace Models; 2.2 Multi-Subspace Models; 2.3 Theoretical Analysis; 2.3.1 Exact Recovery; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR
- 2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 Closed-Form Solutions; 2.3.3 Block-Diagonal Structure; References; 3 Nonlinear Models; 3.1 Kernel Methods; 3.2 Laplacian Based Methods; 3.3 Locally Linear Representation; 3.4 Transformation Invariant Clustering; References; 4 Optimization Algorithms; 4.1 Convex Algorithms; 4.1.1 Accelerated Proximal Gradient; 4.1.2 Frank-Wolfe Algorithm; 4.1.3 Alternating Direction Method; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis
- 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 4.1.5 (Proximal) Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty; 4.2 Nonconvex Algorithms; 4.2.1 Generalized Singular Value Thresholding; 4.2.2 Iteratively Reweighted Nuclear Norm Algorithm; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering
- 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 5.1.2 Experiments
- 5.2 Background Modeling [2]5.2.1 Implementation Details; 5.2.2 Experiments; 5.3 Robust Alignment by Sparse and Low-Rank (RASL) Decomposition [42]; 5.3.1 Implementation Details; 5.3.2 Experiments; 5.4 Transform Invariant Low-Rank Textures (TILT) [58]; 5.5 Motion and Image Segmentation [30,29,4]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix