IEEE Trans Pattern Anal Mach Intell - Half-quadratic based Iterative Minimization for Robust Sparse Representation.

Tópicos

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Resumo

Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explore their relation is still an open problem. In this paper, we develop a half-quadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an L1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an L1-regularized error detection method by learning from uncorrupted data iteratively. We also show that the L1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings.

Resumo Limpo

robust spars represent shown signific potenti solv challeng problem comput vision biometr visual surveil although sever robust spars model propos promis result obtain either error correct error detect learn general framework systemat unifi two aspect explor relat still open problem paper develop halfquadrat hq framework solv robust spars represent problem defin differ kind halfquadrat function propos hq framework applic perform error correct error detect specif use addit form hq propos lregular error correct method iter recov corrupt data error incur nois outlier use multipl form hq propos lregular error detect method learn uncorrupt data iter also show lregular solv softthreshold function dual relationship huber mestim theoret guarante perform robust spars represent term mestim experi robust face recognit sever occlus corrupt valid framework find

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