IEEE Trans Image Process - Graph regularized sparse coding for image representation.

Tópicos

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Resumo

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.

Resumo Limpo

spars code receiv increas amount interest recent year unsupervis learn algorithm find basi set captur highlevel semant data learn spars coordin term basi set origin appli model human visual cortex spars code shown use mani applic howev exist approach spars code fail consid geometr structur data space mani real applic data like resid lowdimension submanifold embed highdimension ambient space shown geometr inform data import discrimin paper propos graph base algorithm call graph regular spars code learn spars represent explicit take account local manifold structur data use graph laplacian smooth oper obtain spars represent vari smooth along geodes data manifold extens experiment result imag classif cluster demonstr effect propos algorithm

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