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» Object Class Recognition by Boosting a Part-Based Model
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CVPR
1997
IEEE
15 years 3 months ago
Learning bilinear models for two-factor problems in vision
In many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. We may want to disentangle illuminant and object colors in color...
William T. Freeman, Joshua B. Tenenbaum
105
Voted
IJSI
2008
156views more  IJSI 2008»
14 years 11 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
16 years 6 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
PAMI
2007
156views more  PAMI 2007»
14 years 10 months ago
Selection and Fusion of Color Models for Image Feature Detection
—The choice of a color model is of great importance for many computer vision algorithms (e.g., feature detection, object recognition, and tracking) as the chosen color model indu...
Harro M. G. Stokman, Theo Gevers
75
Voted
ICIP
2000
IEEE
16 years 13 days ago
Hierarchical Image Probability (HIP) Models
We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vecto...
Clay Spence, Lucas C. Parra, Paul Sajda