IEEE Trans Image Process - Multiple kernel sparse representations for supervised and unsupervised learning.

Tópicos

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Resumo

In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.

Resumo Limpo

complex visual recognit task typic adopt multipl descriptor describ differ aspect imag obtain improv recognit perform descriptor divers form can fuse unifi featur space principl manner use kernel method spars model general well test data can learn unifi kernel space appropri constraint can incorpor applic supervis unsupervis learn paper propos perform spars code dictionari learn multipl kernel space weight ensembl kernel tune base graphembed principl class discrimin maxim propos algorithm dictionari infer use multipl level d subspac cluster kernel space spars code obtain use simpl levelwis pursuit scheme empir result object recognit imag cluster show algorithm outperform exist spars code base approach compar favor stateoftheart method

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