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» Learning in Computer Vision: Some Thoughts
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ICIP
2004
IEEE
14 years 9 months ago
Multi-label SVM active learning for image classification
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categoriz...
Xuchun Li, Lei Wang, Eric Sung
ICMCS
2005
IEEE
110views Multimedia» more  ICMCS 2005»
14 years 1 months ago
Learned color constancy from local correspondences
The ability of humans for color constancy, i.e. the ability to correct for color deviation caused by a different illumination, is far beyond computer vision performances: nowadays...
Tijmen Moerland, Frédéric Jurie
CVPR
2004
IEEE
14 years 9 months ago
Multiscale Conditional Random Fields for Image Labeling
We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set of labels. The features are incorporated into a p...
Miguel Á. Carreira-Perpiñán, ...
CVPR
2012
IEEE
11 years 9 months ago
Bilevel sparse coding for coupled feature spaces
In this paper, we propose a bilevel sparse coding model for coupled feature spaces, where we aim to learn dictionaries for sparse modeling in both spaces while enforcing some desi...
Jianchao Yang, Zhaowen Wang, Zhe Lin, Xianbiao Shu...
ACCV
2007
Springer
14 years 1 months ago
Analyzing Facial Expression by Fusing Manifolds
Feature representation and classification are two major issues in facial expression analysis. In the past, most methods used either holistic or local representation for analysis. ...
Wen-Yan Chang, Chu-Song Chen, Yi-Ping Hung