Unsupervised segmentation of natural images via lossy data compression

By Allen Yuqing Yang, John Wright, Yi Ma, S. Sastry. Computer Vision and Image Understanding, 2008

558 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5f877eba0a5e19ae740db0f0e542b51f9c2f58aa

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In Computer Vision and Pattern Recognition

Citations in the corpus

Segmentation using superpixels: A bipartite graph partitioning approach (2012, methodology): “In most cases, they are used to initialize segmentation [29, 28, 1, 8, 21].”
Contour Detection and Hierarchical Image Segmentation (2011, methodology): “Variants of the Rand Index have been proposed [5], [7] for dealing with the case of multiple ground-truth segmentations.”
Multi-Scale Fusion Subspace Clustering Using Similarity Constraint (2020, background): “…clustering [33] has aroused widespread research interests in unsupervised learning and is successfully exploited to various applications, such as image segmentation [22, 36], motion segmentation [14, 4], image clustering [40, 5, 37, 38, 11], genes expression micro-array clustering [24] and so on.”
Deep Subspace Clustering Networks (2017, background): “, image segmentation [50, 27], motion segmentation [17, 9], and image clustering [14, 10].”
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].”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2013, unknown): “, image segmentation [7], motion segmentation [8], [9], and temporal video segmentation [10]), as illustrated in Figs.”
Segmentation of Natural Images by Texture and Boundary Compression (2010, unknown): “For generic samples from multiple classes, one can estimate the distribution of each class label and then code the membership of the samples using a scheme that is asymptotically optimal for that class distribution (such as the Huffman code used in Yang et al. (2008)).”
From contours to regions: An empirical evaluation (2009, background): “A broad family of approaches to segmentation involve integrating features such as brightness, color, or texture over local image patches and then clustering those features based on, e.g., fitting mixture models [5] [47], mode-finding [10], or graph partitioning [42] [24] [44] [15].”
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images (2014, background): “[28] used sparse representations for image super-resolution by assuming that the high-resolution image patches have a sparse representation with respect to that of low-resolution images.”
Low rank subspace clustering (LRSC) (2014, background): “This problem, known as subspace clustering, finds numerous applications in computer vision, e.g., image segmentation (Yang et al., 2008), motion segmentation (Vidal et al., 2008) and face clustering (Ho et al., 2003), image processing, e.g., image representation and compression (Hong et al., 2006),…”
Sparse subspace clustering (2009, background): “Subspace clustering is an important problem with numerous applications in image processing, e.g. image representation and compression [15] [29], and computer vision, e.g. image/motion/video segmentation [6] [16] [30] [28] [26].”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2012, background): “Index Terms—High-dimensional data, intrinsic low-dimensionality, subspaces, clustering, sparse representation, ‘1-minimization, convex programming, spectral clustering, principal angles, motion segmentation, face clustering Ç”