IEEE Trans Pattern Anal Mach Intell - Learning spectral descriptors for deformable shape correspondence.

Tópicos

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Resumo

Informative and discriminative feature descriptors play a fundamental role in deformable shape analysis. For example, they have been successfully employed in correspondence, registration, and retrieval tasks. In the recent years, significant attention has been devoted to descriptors obtained from the spectral decomposition of the Laplace-Beltrami operator associated with the shape. Notable examples in this family are the heat and the wave kernel signatures. Laplacian-based descriptors achieve state-of-the-art performance in numerous shape analysis tasks; they are computationally efficient, isometry-invariant by construction, and can gracefully cope with a variety of transformations. In this paper, we formulate a generic family of parametric spectral descriptors. We argue that in order to be optimized for a specific task, the descriptor should take into account the statistics of the corpus of shapes to which it is applied (the "signal") and those of the class of transformations to which it is made insensitive (the "noise"). While such statistics are hard to model axiomatically, they can be learned from examples. Following the spirit of the Wiener filter in signal processing, we show a learning scheme for the construction of optimized spectral descriptors and relate it to Mahalanobis metric learning. The superiority of the proposed approach in generating correspondences is demonstrated on synthetic and scanned human figures.

Resumo Limpo

inform discrimin featur descriptor play fundament role deform shape analysi exampl success employ correspond registr retriev task recent year signific attent devot descriptor obtain spectral decomposit laplacebeltrami oper associ shape notabl exampl famili heat wave kernel signatur laplacianbas descriptor achiev stateoftheart perform numer shape analysi task comput effici isometryinvari construct can grace cope varieti transform paper formul generic famili parametr spectral descriptor argu order optim specif task descriptor take account statist corpus shape appli signal class transform made insensit nois statist hard model axiomat can learn exampl follow spirit wiener filter signal process show learn scheme construct optim spectral descriptor relat mahalanobi metric learn superior propos approach generat correspond demonstr synthet scan human figur

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