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» Learning Sample Subspace with Application to Face Detection
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134
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CVPR
2012
IEEE
13 years 3 days ago
Fixed-rank representation for unsupervised visual learning
Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-theart technique...
Risheng Liu, Zhouchen Lin, Fernando De la Torre, Z...
TCSV
2008
195views more  TCSV 2008»
14 years 9 months ago
Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing
Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonst...
Yun Fu, Zhu Li, Junsong Yuan, Ying Wu, Thomas S. H...
CVPR
2010
IEEE
15 years 5 months ago
Spatialized Epitome and Its Applications
Due to the lack of explicit spatial consideration, existing epitome model may fail for image recognition and target detection, which directly motivates us to propose the so-calle...
Xinqi Chu, Shuicheng Yan, Liyuan Li, Kap Luk Chan,...
PR
2006
127views more  PR 2006»
14 years 9 months ago
On solving the face recognition problem with one training sample per subject
The lack of adequate training samples and the considerable variations observed in the available image collections due to aging, illumination and pose variations are the two key te...
Jie Wang, Kostas N. Plataniotis, Juwei Lu, Anastas...
78
Voted
CVPR
2007
IEEE
15 years 4 months ago
Matrix-Structural Learning (MSL) of Cascaded Classifier from Enormous Training Set
Aiming at the problem when both positive and negative training set are enormous, this paper proposes a novel Matrix-Structural Learning (MSL) method, as an extension to Viola and ...
Shengye Yan, Shiguang Shan, Xilin Chen, Wen Gao, J...