Structural Texture Similarity Metrics for Image Analysis and Retrieval

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 identic...

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Bibliographic Details
Published in:IEEE transactions on image processing 2013-07, Vol.22 (7), p.2545-2558
Main Authors: Zujovic, Jana, Pappas, T. N., Neuhoff, D. L.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Correlation
Databases, Factual
Exact sciences and technology
Gray-scale
Humans
Image coding
Image processing
Image Processing, Computer-Assisted - methods
Information theory
Information, signal and communications theory
Measurement
Models, Theoretical
Natural textures
Pattern recognition
perceptual quality
PSNR
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
statistical models
Surface Properties
Telecommunications and information theory
Visual Perception
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2013.2251645