Structural texture similarity metrics for image analysis and retrieval zebros ieee projects

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ZEBROS PROJECTS 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 sub band 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. ZEBROS PROJECTS SOFTWARE BASED Networking

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Abstract Existing System Disadvantages Proposed System Advantages Objective System Requirements System Architecture 2nd Review

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Use case Diagram Class Diagram Collaboration Diagram Sequence Diagram Activity Diagram Testing & test cases Partial Code Screenshot for First two module  Review Document Explanation Final Review

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Office Address: No 4 / Flat No 3D, Sai Kiran Apts, First Main Road, Kasturba Nagar, Chennai-20 web: www.zebros.in e mail : zebrosprojects@gmail.com mob: 99400 98300,9500075001

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Structural texture similarity metrics for image analysis and retrieval zebros ieee projects

Published on Nov 14, 2013

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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 sub band 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.