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CVPR
2011
IEEE
14 years 9 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
VLSISP
2010
254views more  VLSISP 2010»
15 years 8 days ago
Manifold Based Local Classifiers: Linear and Nonlinear Approaches
Abstract In case of insufficient data samples in highdimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nea...
Hakan Cevikalp, Diane Larlus, Marian Neamtu, Bill ...
ECCV
2004
Springer
16 years 3 months ago
Towards Intelligent Mission Profiles of Micro Air Vehicles: Multiscale Viterbi Classification
In this paper, we present a vision system for object recognition in aerial images, which enables broader mission profiles for Micro Air Vehicles (MAVs). The most important factors ...
Sinisa Todorovic, Michael C. Nechyba
CVPR
2000
IEEE
16 years 3 months ago
In Search of Illumination Invariants
We consider the problem of determining functions of an image of an object that are insensitive to illumination changes. We rst show that for an object with Lambertian r e e ctanc ...
Hansen F. Chen, Peter N. Belhumeur, David W. Jacob...
SIGIR
2003
ACM
15 years 7 months ago
Automatic image annotation and retrieval using cross-media relevance models
Libraries have traditionally used manual image annotation for indexing and then later retrieving their image collections. However, manual image annotation is an expensive and labo...
Jiwoon Jeon, Victor Lavrenko, R. Manmatha