Graph regularized sparse coding for image representation

Graph regularized sparse coding for image representation

Sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. Originally applied to modeling the human visual cortex, s...

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Détails bibliographiques
Publié dans:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 20(2011), 5 vom: 15. Mai, Seite 1327-36
Auteur principal: Zheng, Miao (Auteur)
Autres auteurs: Bu, Jiajun, Chen, Chun, Wang, Can, Zhang, Lijun, Qiu, Guang, Cai, Deng
Format: Article en ligne
Langue:English
Publié: 2011
Accès à la collection:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Sujets:Journal Article Research Support, Non-U.S. Gov't
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520 |a Sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. Originally applied to modeling the human visual cortex, sparse coding has been shown useful for many applications. However, most of the existing approaches to sparse coding fail to consider the geometrical structure of the data space. In many real applications, the data is more likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. It has been shown that the geometrical information of the data is important for discrimination. In this paper, we propose a graph based algorithm, called graph regularized sparse coding, to learn the sparse representations that explicitly take into account the local manifold structure of the data. By using graph Laplacian as a smooth operator, the obtained sparse representations vary smoothly along the geodesics of the data manifold. The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Bu, Jiajun  |e verfasserin  |4 aut 
700 1 |a Chen, Chun  |e verfasserin  |4 aut 
700 1 |a Wang, Can  |e verfasserin  |4 aut 
700 1 |a Zhang, Lijun  |e verfasserin  |4 aut 
700 1 |a Qiu, Guang  |e verfasserin  |4 aut 
700 1 |a Cai, Deng  |e verfasserin  |4 aut 
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