Results 1 - 10 of 1,306

Patch Affinity Propagation

by Xibin Zhu, Barbara Hammer
"... Abstract. Affinity propagation constitutes an exemplar based clustering technique which reliably optimizes the quantization error given a matrix of pairwise data dissimilarities by means of the max-sum algorithm for factor graphs. Albeit very efficient for sparse matrices, it displays squared comple ..."
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complexity in the worst case, hence it is not suited as high throughput method due to time and memory constraints. We propose an extension of affinity propagation to patch clustering such that data are treated in chunks of fixed size with limited memory requirements and linear time. We test the suitability

Clustering by passing messages between data points

by Brendan J. Frey, Delbert Dueck - Science , 2007
"... Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such “exemplars ” can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initi ..."
Abstract - Cited by 696 (8 self) - Add to MetaCart
if that initial choice is close to a good solution. We devised a method called “affinity propagation,” which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges

Visual categorization with bags of keypoints

by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cédric Bray - In Workshop on Statistical Learning in Computer Vision, ECCV , 2004
"... Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of im ..."
Abstract - Cited by 1005 (14 self) - Add to MetaCart
Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors

Learning low-level vision

by William T. Freeman, Egon C. Pasztor - International Journal of Computer Vision , 2000
"... We show a learning-based method for low-level vision problems. We set-up a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently prop ..."
Abstract - Cited by 579 (30 self) - Add to MetaCart
We show a learning-based method for low-level vision problems. We set-up a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently

PatchMatch: A Randomized Correspondence Algorithm for . . .

by Connelly Barnes, Eli Shechtman, Adam Finkelstein , Dan B Goldman , 2009
"... This paper presents interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest-neighbor matches between image patches. Previous research in graphics and vision has leveraged such nearest-neighbor searches to provide a variety of high-level digital image ..."
Abstract - Cited by 243 (9 self) - Add to MetaCart
. The key insights driving the algorithm are that some good patch matches can be found via random sampling, and that natural coherence in the imagery allows us to propagate such matches quickly to surrounding areas. We offer theoretical analysis of the convergence properties of the algorithm, as well

Mixture modeling by affinity propagation

by Brendan J. Frey, Delbert Dueck - Advances in Neural Information Processing Systems 18 , 2006
"... Software and demonstrations available at www.psi.toronto.edu Clustering is a fundamental problem in machine learning and has been approached in many ways. Two general and quite different approaches include iteratively fitting a mixture model (e.g., using EM) and linking to-gether pairs of training c ..."
Abstract - Cited by 26 (2 self) - Add to MetaCart
demonstrate affinity prop-agation on the problems of clustering image patches for image segmen-tation and learning mixtures of gene expression models from microar-ray data. We find that affinity propagation obtains better solutions than mixtures of Gaussians, the K-medoids algorithm, spectral clustering

Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions

by Tinne Tuytelaars, Luc Van Gool - In Proc. BMVC , 2000
"... `Invariant regions' are image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. Such regions are then described by a set of invariant features, which makes it relatively easy to match them between views and under changing illum ..."
Abstract - Cited by 218 (7 self) - Add to MetaCart
`Invariant regions' are image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. Such regions are then described by a set of invariant features, which makes it relatively easy to match them between views and under changing

Image completion with structure propagation

by Jian Sun, Lu Yuan, Jiaya Jia, Heung-yeung Shum - ACM Transactions on Graphics , 2005
"... two intersecting lines (green) specified by the user, (c) intermediate result after propagating structure and texture information along the user-specified lines, and (d) final result after filling in the remaining unknown regions by texture propagation. In this paper, we introduce a novel approach t ..."
Abstract - Cited by 133 (4 self) - Add to MetaCart
to image completion, which we call structure propagation. In our system, the user manually specifies important missing structure information by extending a few curves or line segments from the known to the unknown regions. Our approach synthesizes image patches along these user-specified curves

Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure

by Andreas Klaus, et al.
"... A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a selfadapting matching score that maximizes the number of reliable correspondences. The scene structure is modeled by a set of planar surface patches which are estimated using a new technique t ..."
Abstract - Cited by 171 (0 self) - Add to MetaCart
A novel stereo matching algorithm is proposed that utilizes color segmentation on the reference image and a selfadapting matching score that maximizes the number of reliable correspondences. The scene structure is modeled by a set of planar surface patches which are estimated using a new technique

Vertex algebras and algebraic curves

by Edward Frenkel - Mathematical Surveys and Monographs 88 (2001), Amer. Math.Soc. MR1849359 (2003f:17036
"... Vertex operators appeared in the early days of string theory as local operators describing propagation of string states. Mathematical analogues of these operators were discovered in representation theory of affine Kac-Moody algebras in the works of Lepowsky–Wilson [LW] and I. Frenkel–Kac [FK]. In or ..."
Abstract - Cited by 177 (10 self) - Add to MetaCart
Vertex operators appeared in the early days of string theory as local operators describing propagation of string states. Mathematical analogues of these operators were discovered in representation theory of affine Kac-Moody algebras in the works of Lepowsky–Wilson [LW] and I. Frenkel–Kac [FK
Results 1 - 10 of 1,306