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      Image classification using concatenated of co-occurrence matrix features and local ternary patterns

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      ScienceOpen Preprints
      ScienceOpen
      Image processing, Feature extraction, Texture Classification, Local Ternary Pattern, Co-occurrence matrix
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            Abstract

            Texture, color, and shape are the three main components that the human visual brain uses to identify or identify environments and objects. Therefore, tissue classification has been considered by many scientific researchers in the last decade. The texture features can be used in many different vision and machine learning problems. As of now, various methods have been proposed for classifying tissues. In all methods, the accuracy of the classification is a major challenge that needs to be improved. This article presents a new method based on a combination of two efficient tissue descriptors, the co-occurrence matrix and local ternary patterns (LTP). First, the local binary pattern and LTP are performed to extract information from the local tissue. In the next step, a subset of statistical properties is extracted from the gray surface concurrency matrices. Finally, the interconnected features are used to teach classification. Performance is evaluated for accuracy on the Brodatz reference data set. The experimental results show that the proposed method offers a higher degree of classification compared to some advanced methods.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            15 March 2021
            Affiliations
            [1 ] Department of electronic engineering, Online computer vision research group
            Article
            10.14293/S2199-1006.1.SOR-.PPLMNCJ.v1
            0e966afd-8b54-4679-811b-a3ec06ac5b61

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Computer vision & Pattern recognition
            Image processing,Feature extraction,Texture Classification,Local Ternary Pattern,Co-occurrence matrix

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