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» Unsupervised Learning of Finite Mixture Models
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CVPR
2009
IEEE
15 years 2 months ago
Learning color and locality cues for moving object detection and segmentation
This paper presents an algorithm for automatically detecting and segmenting a moving object from a monocular video. Detecting and segmenting a moving object from a video with limit...
Feng Liu (University of Wisconsin-Madison), Michae...
ECML
2007
Springer
14 years 1 months ago
Spectral Clustering and Embedding with Hidden Markov Models
Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering ...
Tony Jebara, Yingbo Song, Kapil Thadani
NIPS
2001
13 years 9 months ago
Global Coordination of Local Linear Models
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a glob...
Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinto...
CVPR
2000
IEEE
14 years 9 months ago
Towards Automatic Discovery of Object Categories
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Markus Weber, Max Welling, Pietro Perona
CDC
2009
IEEE
149views Control Systems» more  CDC 2009»
13 years 8 months ago
Context-dependent multi-class classification with unknown observation and class distributions with applications to bioinformatic
We consider the multi-class classification problem, based on vector observation sequences, where the conditional (given class observations) probability distributions for each class...
Alex S. Baras, John S. Baras