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Structural Texture Similarity Metrics for Image Analysis and Retrieval

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3 Author(s)
Zujovic, J. ; Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA ; Pappas, T.N. ; Neuhoff, D.L.

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.

Published in:

Image Processing, IEEE Transactions on  (Volume:22 ,  Issue: 7 )