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» 2LDA: Segmentation for Recognition
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CVPR
2009
IEEE
15 years 4 months 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...
CVPR
2006
IEEE
14 years 11 months ago
Counting Crowded Moving Objects
In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably wi...
Vincent Rabaud, Serge Belongie
CVPR
2007
IEEE
14 years 11 months ago
Learning and Matching Line Aspects for Articulated Objects
Traditional aspect graphs are topology-based and are impractical for articulated objects. In this work we learn a small number of aspects, or prototypical views, from video data. ...
Xiaofeng Ren
ICCV
2003
IEEE
14 years 11 months ago
Graph Partition by Swendsen-Wang Cuts
Vision tasks, such as segmentation, grouping, recognition, can be formulated as graph partition problems. The recent literature witnessed two popular graph cut algorithms: the Ncu...
Adrian Barbu, Song Chun Zhu
ICCV
1999
IEEE
14 years 11 months ago
Projective Alignment with Regions
?We have recently proposed an approach to recognition that uses regions to determine the pose of objects while allowing for partial occlusion of the regions. Regions introduce an a...
Ronen Basri, David W. Jacobs