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» Pruning Training Sets for Learning of Object Categories
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ICCV
2009
IEEE
15 years 2 months ago
Active Skeleton for Non-rigid Object Detection
We present a shape-based algorithm for detecting and recognizing non-rigid objects from natural images. The existing literature in this domain often cannot model the objects ver...
Xiang Bai, Xinggang Wang, Longin Jan Latecki, Weny...
KDD
2009
ACM
196views Data Mining» more  KDD 2009»
14 years 3 months ago
WhereNext: a location predictor on trajectory pattern mining
The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data. This side effect provides the opportunity for innovative meth...
Anna Monreale, Fabio Pinelli, Roberto Trasarti, Fo...
DICTA
2008
13 years 10 months ago
Object Detection Using Multi-local Feature Manifolds
Many object categories are better characterized by the shape of their contour than by local appearance properties like texture or color. Multi-local features are designed in order...
Oscar M. Danielsson, Stefan Carlsson, Josephine Su...
IVC
2007
97views more  IVC 2007»
13 years 8 months ago
Stochastic exploration and active learning for image retrieval
This paper deals with content-based image retrieval. When the user is looking for large categories, statistical classification techniques are efficient as soon as the training se...
Matthieu Cord, Philippe Henri Gosselin, Sylvie Phi...
ICCV
2009
IEEE
1419views Computer Vision» more  ICCV 2009»
15 years 2 months ago
On Feature Combination for Multiclass Object Classification
A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. Whil...
Peter Gehler, Sebastian Nowozin