Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications

The symmetric positive-definite (SPD) matrix, as a connected Riemannian manifold, has become increasingly popular for encoding image information. Most existing sparse models are still primarily developed in the Euclidean space. They do not consider the non-linear geometrical structure of the data sp...

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Vydáno v:	IEEE transactions on image processing Ročník 24; číslo 11; s. 3729 - 3741
Hlavní autoři:	Wu, Yuwei, Jia, Yunde, Li, Peihua, Zhang, Jian, Yuan, Junsong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2015
Témata:	Algorithms Biometric Identification Covariance matrices Databases, Factual Dictionaries Face recognition Geometry Humans Image classification Image Processing, Computer-Assisted - methods Kernel Kernel sparse coding Machine Learning Manifolds Measurement Region covariance descriptor Riemannian manifold Sparse matrices Symmetric Positive Definite Matrices Visual tracking Kernel sparse coding image classification visual tracking face recognition region covariance descriptor symmetric positive definite matrices Riemannian manifold
ISSN:	1057-7149, 1941-0042
On-line přístup:	Získat plný text
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