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ICCV
2009
IEEE
1556views Computer Vision» more  ICCV 2009»
16 years 12 months ago
Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of th...
Oncel Tuzel, Fatih Porikli, Peter Meer
CVPR
2004
IEEE
16 years 8 months ago
Random Sampling LDA for Face Recognition
Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face recognition. However, It often suffers from the small sample size problem when dealing with t...
Xiaogang Wang, Xiaoou Tang
CVPR
2007
IEEE
16 years 8 months ago
Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods
Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more approp...
Pablo Arias, Gregory Randall, Guillermo Sapiro
CVPR
2008
IEEE
16 years 8 months ago
Robust statistics on Riemannian manifolds via the geometric median
The geometric median is a classic robust estimator of centrality for data in Euclidean spaces. In this paper we formulate the geometric median of data on a Riemannian manifold as ...
P. Thomas Fletcher, Suresh Venkatasubramanian, Sar...
ECCV
2004
Springer
16 years 8 months ago
Face Recognition with Local Binary Patterns
In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small...
Abdenour Hadid, Matti Pietikäinen, Timo Ahone...