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» Pruning Training Sets for Learning of Object Categories
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MM
2006
ACM
152views Multimedia» more  MM 2006»
14 years 1 months ago
Local image representations using pruned salient points with applications to CBIR
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture import...
Hui Zhang, Rouhollah Rahmani, Sharath R. Cholleti,...
CVPR
2011
IEEE
13 years 3 months ago
From Region Similarity to Category Discovery
The goal of object category discovery is to automatically identify groups of image regions which belong to some new, previously unseen category. This task is typically performed i...
Carolina Galleguillos, Brian McFee, Serge Belongie...
ICML
2002
IEEE
14 years 8 months ago
Pruning Improves Heuristic Search for Cost-Sensitive Learning
This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to ...
Valentina Bayer Zubek, Thomas G. Dietterich
CVPR
2008
IEEE
14 years 9 months ago
Dynamic visual category learning
Dynamic visual category learning calls for efficient adaptation as new training images become available or new categories are defined, existing training images or categories becom...
Tom Yeh, Trevor Darrell
CVPR
2007
IEEE
14 years 2 months ago
Local Ensemble Kernel Learning for Object Category Recognition
This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass featu...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh