IEEE Trans Image Process - Improving shape retrieval by spectral matching and meta similarity.

Tópicos

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Resumo

We propose two computational approaches for improving the retrieval of planar shapes. First, we suggest a geometrically motivated quadratic similarity measure, that is optimized by way of spectral relaxation of a quadratic assignment. By utilizing state-of-the-art shape descriptors and a pairwise serialization constraint, we derive a formulation that is resilient to boundary noise, articulations and nonrigid deformations. This allows both shape matching and retrieval. We also introduce a shape meta-similarity measure that agglomerates pairwise shape similarities and improves the retrieval accuracy. When applied to the MPEG-7 shape dataset in conjunction with the proposed geometric matching scheme, we obtained a retrieval rate of 92.5%.

Resumo Limpo

propos two comput approach improv retriev planar shape first suggest geometr motiv quadrat similar measur optim way spectral relax quadrat assign util stateoftheart shape descriptor pairwis serial constraint deriv formul resili boundari nois articul nonrigid deform allow shape match retriev also introduc shape metasimilar measur agglomer pairwis shape similar improv retriev accuraci appli mpeg shape dataset conjunct propos geometr match scheme obtain retriev rate

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