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IJCV
2000
164views more  IJCV 2000»
13 years 5 months ago
Probabilistic Modeling and Recognition of 3-D Objects
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical too...
Joachim Hornegger, Heinrich Niemann
CVPR
2009
IEEE
15 years 16 days ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
MM
2006
ACM
152views Multimedia» more  MM 2006»
13 years 11 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,...
IJCV
2011
264views more  IJCV 2011»
13 years 11 days ago
Cost-Sensitive Active Visual Category Learning
Abstract We present an active learning framework that predicts the tradeoff between the effort and information gain associated with a candidate image annotation, thereby ranking un...
Sudheendra Vijayanarasimhan, Kristen Grauman
VLSISP
2010
254views more  VLSISP 2010»
13 years 3 months 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 ...