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» Principal Manifolds and Probabilistic Subspaces for Visual R...
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TKDE
2008
195views more  TKDE 2008»
13 years 7 months ago
Learning a Maximum Margin Subspace for Image Retrieval
One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap...
Xiaofei He, Deng Cai, Jiawei Han
PAMI
2012
11 years 10 months ago
Probabilistic Models for Inference about Identity
—Many face recognition algorithms use “distance-based” methods: Feature vectors are extracted from each face and distances in feature space are compared to determine matches....
Simon Prince, Peng Li, Yun Fu, Umar Mohammed, Jame...
PR
2007
96views more  PR 2007»
13 years 7 months ago
Weighted and robust learning of subspace representations
A reliable system for visual learning and recognition should enable a selective treatment of individual parts of input data and should successfully deal with noise and occlusions....
Danijel Skocaj, Ales Leonardis, Horst Bischof
CVPR
2003
IEEE
14 years 9 months ago
Kernel Principal Angles for Classification Machines with Applications to Image Sequence Interpretation
We consider the problem of learning with instances defined over a space of sets of vectors. We derive a new positive definite kernel f(A B) defined over pairs of matrices A B base...
Lior Wolf, Amnon Shashua
IJON
1998
102views more  IJON 1998»
13 years 7 months ago
Developments of the generative topographic mapping
The Generative Topographic Mapping (GTM) model was introduced by 7) as a probabilistic re-formulation of the self-organizing map (SOM). It offers a number of advantages compared ...
Christopher M. Bishop, Markus Svensén, Chri...