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» Optimizing Learning in Image Retrieval
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CVPR
2006
IEEE
14 years 7 months ago
Semi-Supervised Classification Using Linear Neighborhood Propagation
We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong...
JMLR
2008
168views more  JMLR 2008»
13 years 5 months ago
Max-margin Classification of Data with Absent Features
We consider the problem of learning classifiers in structured domains, where some objects have a subset of features that are inherently absent due to complex relationships between...
Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbe...
ECCV
2008
Springer
14 years 7 months ago
Unified Crowd Segmentation
Abstract. This paper presents a unified approach to crowd segmentation. A global solution is generated using an Expectation Maximization framework. Initially, a head and shoulder d...
Gianfranco Doretto, Jens Rittscher, Nils Krahnstoe...
PAMI
2008
202views more  PAMI 2008»
13 years 5 months ago
Metamorphs: Deformable Shape and Appearance Models
This paper presents a new deformable modeling strategy aimed at integrating shape and appearance in a unified space. If we think traditional deformable models as "active cont...
Xiaolei Huang, Dimitris N. Metaxas
CVPR
2012
IEEE
11 years 7 months ago
A learning-based framework for depth ordering
Depth ordering is instrumental for understanding the 3D geometry of an image. We as humans are surprisingly good ordering even with abstract 2D line drawings. In this paper we pro...
Zhaoyin Jia, Andrew C. Gallagher, Yao-Jen Chang, T...