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» Learning from Ambiguously Labeled Images
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CVPR
2007
IEEE
14 years 10 months ago
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
Li-Jia Li, Gang Wang, Fei-Fei Li 0002
MICCAI
2005
Springer
14 years 9 months ago
Efficient Learning by Combining Confidence-Rated Classifiers to Incorporate Unlabeled Medical Data
Abstract. In this paper, we propose a new dynamic learning framework that requires a small amount of labeled data in the beginning, then incrementally discovers informative unlabel...
Weijun He, Xiaolei Huang, Dimitris N. Metaxas, Xia...
COLING
2010
13 years 3 months ago
EMMA: A novel Evaluation Metric for Morphological Analysis
We present a novel Evaluation Metric for Morphological Analysis (EMMA) that is both linguistically appealing and empirically sound. EMMA uses a graphbased assignment algorithm, op...
Sebastian Spiegler, Christian Monson
ECCV
2010
Springer
13 years 10 months ago
Improving the Fisher Kernel for Large-Scale Image Classification
Abstract. The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was s...
CVPR
2008
IEEE
14 years 10 months ago
Parameterized Kernel Principal Component Analysis: Theory and applications to supervised and unsupervised image alignment
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
Fernando De la Torre, Minh Hoai Nguyen