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» Top-k learning to rank: labeling, ranking and evaluation
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TRECVID
2008
14 years 11 months ago
LIG and LIRIS at TRECVID 2008: High Level Feature Extraction and Collaborative Annotation
This paper describes our participations of LIG and LIRIS to the TRECVID 2008 High Level Features detection task. We evaluated several fusion strategies and especially rank fusion....
IJDMMM
2008
124views more  IJDMMM 2008»
14 years 9 months ago
Completing missing views for multiple sources of web media
: Combining multiple data sources, each with its own features, to achieve optimal inference has received a lot of attention in recent years. In inference from multiple data sources...
Shankara B. Subramanya, Zheshen Wang, Baoxin Li, H...
PAMI
2006
125views more  PAMI 2006»
14 years 9 months ago
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring dif...
Cees Snoek, Marcel Worring, Jan-Mark Geusebroek, D...
NAACL
2010
14 years 7 months ago
Hitting the Right Paraphrases in Good Time
We present a random-walk-based approach to learning paraphrases from bilingual parallel corpora. The corpora are represented as a graph in which a node corresponds to a phrase, an...
Stanley Kok, Chris Brockett
CVPR
2011
IEEE
14 years 1 months ago
Effective 3D Object Detection and Regression Using Probabilistic Segmentation Features in CT Images
3D object detection and importance regression/ranking are at the core for semantically interpreting 3D medical images of computer aided diagnosis (CAD). In this paper, we propose ...
Le Lu, Jinbo Bi, Matthias Wolf, Marcos Salganicoff