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ECCV
2010
Springer
14 years 9 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
JCB
1998
92views more  JCB 1998»
14 years 9 months ago
Homology Detection via Family Pairwise Search
The function of an unknown biological sequence can often be accurately inferred by identifying sequences homologous to the original sequence. Given a query set of known homologs, ...
William Noble Grundy
FGR
2011
IEEE
288views Biometrics» more  FGR 2011»
14 years 1 months ago
Hierarchical CRF with product label spaces for parts-based models
— Non-rigid object detection is a challenging open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, humancomput...
Gemma Roig, Xavier Boix Bosch, Fernando De la Torr...
ICALP
2009
Springer
15 years 10 months ago
Counting Subgraphs via Homomorphisms
We introduce a generic approach for counting subgraphs in a graph. The main idea is to relate counting subgraphs to counting graph homomorphisms. This approach provides new algori...
Omid Amini, Fedor V. Fomin, Saket Saurabh
CVPR
2009
IEEE
16 years 4 months ago
A Min-Max Framework of Cascaded Classifier with Multiple Instance Learning for Computer Aided Diagnosis
The computer aided diagnosis (CAD) problems of detecting potentially diseased structures from medical images are typically distinguished by the following challenging characterist...
Dijia Wu (Rensselaer Polytechnic Institute), Jinbo...