Structural texture similarity metrics for image analysis and retrieval

Jana Zujovic, Thrasyvoulos N. Pappas, David L. Neuhoff

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6476011
Pages (from-to)2545-2558
Number of pages14
JournalIEEE Transactions on Image Processing
Volume22
Issue number7
DOIs
StatePublished - May 23 2013

Keywords

  • Natural textures
  • perceptual quality
  • statistical models

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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