Clustering appearances of objects under varying illumination conditions

By Jeffrey Ho, Ming-Hsuan Yang, Jongwoo Lim, Kuang-chih Lee, David J. Kriegman. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003

593 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a94c7e602c2cfd85343a09662b90206f2494d239

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In Computer Vision and Pattern Recognition

Citations in the corpus

Sparse Subspace Clustering: Algorithm, Theory, and Applications (2013, methodology): “Iterative approaches, such as Ksubspaces [12], [13] and median K-flats [14], alternate between assigning points to subspaces and fitting a subspace to each cluster.”
Generalized principal component analysis (GPCA) (2005, methodology): “2. K-subspaces. Given an initial estimate for the subspace bases,thisalgorithmalternatesbetweenclusteringthe datapointsusingthedistanceresidualtothedifferent subspaces and computing a basis for each subspace using standard PCA. See [ 10 ] for further details.”
Robust Recovery of Subspace Structures by Low-Rank Representation (2010, methodology): “The estimation can be performed either by using the Expectation Maximization (EM) algorithm to find a maximum likelihood estimate, as done in [21], or by iteratively findi g a min-max estimate, as adopted by K-subspaces [8] and Random Sample Consensus (RANSAC) [10].”
Locality Preserving Projections (2003, methodology): “3 Face Clustering Using Laplacianfaces In this Section, we investigate the use of our algorithm on face clustering [26][36][49].”
Latent Low-Rank Representation for subspace segmentation and feature extraction (2011, background): “In computer vision, subspace segmentation has been widely studied (e.g., [2, 3, 5, 7, 11, 17, 18, 19, 21, 22, 27]) owing to its numerous applications, such as motion segmentation [6, 21, 29], face recognition [26, 28] and image segmentation [15, 18].”
A Geometric Analysis of Subspace Clustering with Outliers (2011, background): “Other applications of subspace clustering in computer vision include image segmentation [48], face clustering [18], image representation and compression [19], and systems theory [44].”
Maximum Correntropy Criterion for Robust Face Recognition (2011, unknown): “…rewritten as r NF S c ðyÞ ¼ y À X c ðX T c X c Þ À1 X T c y 2 : ð10Þ If the columns of X c are orthonormal, we have the nearest subspace algorithm [37] that solves the following optimization problem: r NS c ðyÞ ¼ À I À X c X T c Á y 2 : ð11Þ After the residual or distance has been calculated,…”
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression (2007, background): “In this context, vector quantization (VQ) can be viewed as the special case of ﬁtting the data with zero-dimensional (afﬁne) subspaces [10].”
Robust Subspace Segmentation by Low-Rank Representation (2010, methodology): “The estimation can be performed either by using the Expectation Maximization (EM) algorithm to find a maximum likelihood estimate , as done in (Gruber & Weiss, 2004), or by iteratively finding a min-max estimate, as adopted by Ksubspaces (Ho et al., 2003) and Random Sample Consensus (RANSAC) (Fischl”
Robust Computation of Linear Models by Convex Relaxation (2012, background): “Illumination models Images of a face – or any Lambertian object – viewed under different illumination conditions lie near a nine-dimensional subspace [3,28,38].”
Low rank subspace clustering (LRSC) (2014, background): “For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage.”
See all by looking at a few: Sparse modeling for finding representative objects (2012, methodology): “We evaluate the classification performance using several standard classification algorithms: Nearest Neighbor (NN) [9], Nearest Subspace (NS) [22], Sparse Representation-based Classification (SRC) [40], and Linear Support Vector Machine (SVM) [9].”
Generalized Transfer Subspace Learning Through Low-Rank Constraint (2014, background): “Finding an appropriate subspace for each datum forms a problem called subspace segmentation, which has been of great interest in the past decade (Ho et al. 2003; Lu and Vidal 2006).”
Robust and Efficient Subspace Segmentation via Least Squares Regression (2012, unknown): “image representation [1], cl ustering [2] and motion segmentation [3] [4].”
Kernel Sparse Representation for Image Classification and Face Recognition (2010, methodology): “Usually Nearest Neighbor (NN) [5] and Nearest Subspace(NS) [11] are used.”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2012, methodology): “Iterative approaches, such as Ksubspaces [12], [13] and median K-flats [14], alternate between assigning points to subspaces and fitting a subspace to each cluster.”
Robust recovery of multiple subspaces by geometric l_p minimization (2011, methodology): “TheK-subspaces algorithm [5, 10, 22] is the most basic heuristic for HLM, and it suggests an iterative procedure attempting to minimize the energy (1) with p= 2.”