Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression

By Yi Ma, Harm Derksen, Wei Hong, John Wright. 2007

537 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering (2017, background): “The task of subspace clustering is to find a low-dimensional subspace to fit each group of data points [37, 38, 39, 40].”
Abnormal event detection in crowded scenes using sparse representation (2013, unknown): “Depending on the sparse reconstruction coefficients, sparse representation has also been used for many matching and classification applications in computer vision domain, such as object tracking [24], object or face recognition [22], image inpainting [20].”
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration (2007, methodology): “As objects may be separable by any of a variety of cues, be it intensity, color, texture, or boundary continuity, many segmentation algorithms were developed and a variety of techniques were utilized including: clustering [1], [2], Markov random fields [3], [4], variational methods [5], [6], and lev”
Compressive Sensing via Nonlocal Low-Rank Regularization (2014, methodology): “In this paper, we will present a unified variational framework for CS recovery exploiting the nonlocal structured sparsity via low-rank approximation.”
Deep Subspace Clustering Networks (2017, background): “, image segmentation [50, 27], motion segmentation [17, 9], and image clustering [14, 10].”
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].”
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories (2010, methodology): “Nevertheless, an agglomerative algorithm, proposed in [ 20 ], has been shown to be very effective for minimizing (7).”
Frustratingly Easy Transferability Estimation (2021, background): “Coding rate has been verified to be qualified even for samples that are in high-dimensional feature representations or from a non-Gaussian distribution [18], which is often the case for features by deep neural networks.”
Sparse subspace clustering (2009, methodology): “Informationtheoretic approaches, such as Agglomerative Lossy Compression (ALC) [17], model each subspace with a degenerate Gaussian, and look for the segmentation of the data that minimizes the coding length needed to fit these points with a mixture of Gaussians.”
On the Role of Sparse and Redundant Representations in Image Processing (2010, methodology): “... each to a different matrix Qp. As the range of each matrix Qp is a low-dimensional subspace, we essentially use an arrangement of many lowdimensional subspaces to approximate the distribution of all signals X . Such a model has been proposed in recent years as a generalization to principal compo”
Unsupervised segmentation of natural images via lossy data compression (2008, methodology): “It is studied in [16] that (5) can accurately recover the true value of e for the simulated Gaussian mixture models by simply setting k = 1.”
Mapping Natural Image Patches by Explicit and Implicit Manifolds (2007, background): “Recently, there has been some work on clustering data by generalized PCA [9], which assumes linear subspaces, which are explicit manifolds.”
Robust Subspace Clustering (2013, background): “Two such approaches are mixtures of probabilistic PCA (MPPCA) [50] and agglomerative lossy compression (ALC) [34].”
Multi-task low-rank affinity pursuit for image segmentation (2011, background): “However, as a data clustering problem, image segmentation performance heavily depends on the choice of the feature space.”
Segmentation of Natural Images by Texture and Boundary Compression (2010, unknown): “Recently, an objective metric based on the notion of lossy minimum description length (MDL) has been proposed for evaluating clustering of general mixed data (Ma et al. 2007).”