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» Learning Mid-Level Features For Recognition
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ICPR
2000
IEEE
13 years 12 months ago
Feature Learning for Recognition with Bayesian Networks
Many realistic visual recognition tasks are “open” in the sense that the number and nature of the categories to be learned are not initially known, and there is no closed set ...
Justus H. Piater, Roderic A. Grupen
IJDAR
2010
169views more  IJDAR 2010»
13 years 6 months ago
A Bayesian network for combining descriptors: application to symbol recognition
Inthispaper,weproposeadescriptorcombination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This ...
Sabine Barrat, Salvatore Tabbone
ICIP
2010
IEEE
13 years 5 months ago
Learning image similarities via Probabilistic Feature Matching
In this paper, we propose a novel image similarity learning approach based on Probabilistic Feature Matching (PFM). We consider the matching process as the bipartite graph matchin...
Ziming Zhang, Ze-Nian Li, Mark S. Drew
TKDE
2011
479views more  TKDE 2011»
13 years 2 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang
ICIP
2009
IEEE
13 years 5 months ago
A food image recognition system with Multiple Kernel Learning
Since health care on foods is drawing people's attention recently, a system that can record everyday meals easily is being awaited. In this paper, we propose an automatic foo...
Taichi Joutou, Keiji Yanai