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NIPS
2001
13 years 9 months ago
Quantizing Density Estimators
We suggest a nonparametric framework for unsupervised learning of projection models in terms of density estimation on quantized sample spaces. The objective is not to optimally re...
Peter Meinicke, Helge Ritter
CVPR
2009
IEEE
15 years 3 months ago
Learning Invariant Features Through Topographic Filter Maps
Several recently-proposed architectures for highperformance object recognition are composed of two main stages: a feature extraction stage that extracts locallyinvariant feature...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergu...
ECCV
2006
Springer
14 years 9 months ago
Sparse Flexible Models of Local Features
Abstract. In recent years there has been growing interest in recognition models using local image features for applications ranging from long range motion matching to object class ...
Gustavo Carneiro, David Lowe
SLSFS
2005
Springer
14 years 1 months ago
Constructing Visual Models with a Latent Space Approach
We propose the use of latent space models applied to local invariant features for object classification. We investigate whether using latent space models enables to learn patterns...
Florent Monay, Pedro Quelhas, Daniel Gatica-Perez,...
CVPR
2005
IEEE
14 years 10 months ago
Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors
We present a novel approach for fast object class recognition incorporating contextual information into boosting. The object is represented as a constellation of generalized corre...
Jaume Amores, Nicu Sebe, Petia Radeva