Incremental tensor subspace learning and its applications to foreground segmentation and tracking

DOI10.1007/s11263-010-0399-6zbMath1235.68269OpenAlexW2112755387MaRDI QIDQ408862

Xinchu Shi, Weiming Hu, Xi Li, Stephen J. Maybank, Xiao-Qin Zhang

Publication date: 12 April 2012

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-010-0399-6

zbMATH Keywords

trackingincremental learningforeground segmentationtensor subspace

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Machine vision and scene understanding (68T45)

Related Items (8)

Several efficient iterative algorithms for solving nonlinear tensor equation \(\mathcal{X} + \mathcal{A}^T \ast_N \mathcal{X}^{-1} \ast_N \mathcal{A} = \mathcal{I}\) with Einstein product ⋮ Incremental algorithms for truncated higher-order singular value decompositions ⋮ Low-rank representation-based object tracking using multitask feature learning with joint sparsity ⋮ A parallel search strategy based on sparse representation for infrared target tracking ⋮ Bounding multiple Gaussians uncertainty with application to object tracking ⋮ Triangular decomposition of CP factors of a third-order tensor with application to solving nonlinear systems of equations ⋮ Incremental CP Tensor Decomposition by Alternating Minimization Method ⋮ Solving tensor E-eigenvalue problem faster

Cites Work

• On incremental and robust subspace learning
• Sequential Karhunen-Loeve basis extraction and its application to images
• On the Best Rank-1 and Rank-(R₁ ,R₂ ,. . .,R_N) Approximation of Higher-Order Tensors
• Downdating the Singular Value Decomposition
• Tracking Nonstationary Visual Appearances by Data-Driven Adaptation
• Generalized low rank approximations of matrices
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