IEEE Trans Image Process - Efficient texture image retrieval using copulas in a Bayesian framework.

Tópicos

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Resumo

In this paper, we investigate a novel joint statistical model for subband coefficient magnitudes of the dual-tree complex wavelet transform, which is then coupled to a Bayesian framework for content-based image retrieval. The joint model allows to capture the association among transform coefficients of the same decomposition scale and different color channels. It further facilitates to incorporate recent research work on modeling marginal coefficient distributions. We demonstrate the applicability of the novel model in the context of color texture retrieval on four texture image databases and compare retrieval performance to a collection of state-of-the-art approaches in the field. Our experiments further include a thorough computational analysis of the main building blocks, runtime measurements, and an analysis of storage requirements. Eventually, we identify a model configuration with low storage requirements, competitive retrieval accuracy, and a runtime behavior, which enables the deployment even on large image databases.

Resumo Limpo

paper investig novel joint statist model subband coeffici magnitud dualtre complex wavelet transform coupl bayesian framework contentbas imag retriev joint model allow captur associ among transform coeffici decomposit scale differ color channel facilit incorpor recent research work model margin coeffici distribut demonstr applic novel model context color textur retriev four textur imag databas compar retriev perform collect stateoftheart approach field experi includ thorough comput analysi main build block runtim measur analysi storag requir eventu identifi model configur low storag requir competit retriev accuraci runtim behavior enabl deploy even larg imag databas

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