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CVPR
1999
IEEE
16 years 4 months ago
Histogram Clustering for Unsupervised Image Segmentation
This paper introduces a novel statistical mixture model for probabilistic grouping of distributional histogram data. Adopting the Bayesian framework, we propose to perform anneale...
Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann
105
Voted
ICCV
2003
IEEE
16 years 4 months ago
Bayesian Clustering of Optical Flow Fields
We present a method for unsupervised learning of classes of motions in video. We project optical flow fields to a complete, orthogonal, a-priori set of basis functions in a probab...
Jesse Hoey, James J. Little
115
Voted
ICIP
2008
IEEE
16 years 4 months ago
Kernel-based high-dimensional histogram estimation for visual tracking
We propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters suc...
Allen Tannenbaum, James G. Malcolm, Peter Karasev
95
Voted
ICIP
2005
IEEE
16 years 4 months ago
Variational segmentation of color images
A variational Bayesian framework is employed in the paper for image segmentation using color clustering. A Gaussian mixture model is used to represent color distributions. Variati...
Nikolaos Nasios, Adrian G. Bors
110
Voted
ICML
2008
IEEE
16 years 3 months ago
A decoupled approach to exemplar-based unsupervised learning
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a convex optimization problem. Convexity is achieved by restricting the set of possi...
Gökhan H. Bakir, Sebastian Nowozin