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» Learning the Relative Importance of Features in Image Data
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ECCV
2004
Springer
14 years 9 months ago
A Constrained Semi-supervised Learning Approach to Data Association
Data association (obtaining correspondences) is a ubiquitous problem in computer vision. It appears when matching image features across multiple images, matching image features to ...
Hendrik Kück, Nando de Freitas, Peter Carbone...
JMLR
2010
147views more  JMLR 2010»
13 years 2 months ago
Image Denoising with Kernels Based on Natural Image Relations
A successful class of image denoising methods is based on Bayesian approaches working in wavelet representations. The performance of these methods improves when relations among th...
Valero Laparra, Juan Gutierrez, Gustavo Camps-Vall...
ML
2006
ACM
110views Machine Learning» more  ML 2006»
13 years 7 months ago
Distribution-based aggregation for relational learning with identifier attributes
Abstract Identifier attributes--very high-dimensional categorical attributes such as particular product ids or people's names--rarely are incorporated in statistical modeling....
Claudia Perlich, Foster J. Provost
LREC
2008
120views Education» more  LREC 2008»
13 years 8 months ago
Exploiting the Role of Position Feature in Chinese Relation Extraction
Relation extraction is the task of finding pre-defined semantic relations between two entities or entity mentions from text. Many methods, such as feature-based and kernel-based m...
Peng Zhang, Wenjie Li, Furu Wei, Qin Lu, Yuexian H...
CVPR
2006
IEEE
14 years 9 months ago
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Kristen Grauman, Trevor Darrell