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» Learning Models for Object Recognition
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CVPR
2006
IEEE
16 years 5 months ago
Using Dependent Regions for Object Categorization in a Generative Framework
"Bag of words" models have enjoyed much attention and achieved good performances in recent studies of object categorization. In most of these works, local patches are mo...
Gang Wang, Ye Zhang, Fei-Fei Li 0002
IJCV
2010
152views more  IJCV 2010»
15 years 1 months ago
Learning Articulated Structure and Motion
Humans demonstrate a remarkable ability to parse complicated motion sequences into their constituent structures and motions. We investigate this problem, attempting to learn the st...
David A. Ross, Daniel Tarlow, Richard S. Zemel
123
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ICPR
2000
IEEE
16 years 4 months ago
Experiments with an Extended Tangent Distance
Invariance is an important aspect in image object recognition. We present results obtained with an extended tangent distance incorporated in a kernel density based Bayesian classi...
Daniel Keysers, Hermann Ney, Jörg Dahmen, Tho...
JMLR
2006
120views more  JMLR 2006»
15 years 3 months ago
Learning Parts-Based Representations of Data
Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can w...
David A. Ross, Richard S. Zemel
CHI
1999
ACM
15 years 7 months ago
Learning and Performing by Exploration: Label Quality Measured by Latent Semantic Analysis
Models of learning and performing by exploration assume that the semantic distance between task descriptions and screen labels controls in part the usersÕ search strategies. Neve...
Rodolfo Soto