title: Structural texture similarity metrics for image analysis and retrieval.
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.
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA. jana.ehmann@huawei.com
authors.forename: Jana
authors.initials: J
authors.lastname: Zujovic
authors.forename: Thrasyvoulos N
authors.initials: TN
authors.lastname: Pappas
authors.forename: David L
authors.initials: DL
authors.lastname: Neuhoff
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D013499:Surface Properties; D014796:Visual Perception