IEEE Trans Image Process - Structural texture similarity metrics for image analysis and retrieval.

Tópicos

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Resumo

We develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of "known-item search," the retrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.

Resumo Limpo

develop new metric textur similar account human visual percept stochast natur textur metric reli entir local imag statist allow substanti pointbypoint deviat textur accord human judgment essenti ident propos metric extend idea structur similar guid research textur analysissynthesi implement use steerabl filter decomposit incorpor concis set subband statist comput global slide window conduct systemat test investig metric perform context knownitem search retriev textur ident queri textur elimin need cumbersom subject test thus enabl comparison human perform larg databas experiment result indic propos metric outperform peak signaltonois ratio psnr structur similar metric ssim variat well stateoftheart textur classif metric use standard statist measur

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