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      Energy analysis of image descriptors in texture classification

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            Abstract

            Texture play important role in image description process. Texture classification is one of the problems which have been paid much attention on by computer vision scientists in last decade. If texture classification is done accurately, it can be used in many problems such as skin detection, surface defect detection, medical image analysis, gender identification, human identification, etc. Since now, many approaches are proposed to perform it. Most of them have tried to extract discriminative features to separate different texture types accurately. This paper has proposed an approach based on energy analysis of some efficient image descriptors such as median binary pattern, Local binary pattern and Gray Level Co-occurrence matrix. Next, by concatenating extracted features, a discriminative feature vector is defined. Finally, classifier is used to classify texture types. Although, this approach is a general one and is could be used in different applications. In the result part the proposed approach has been evaluated on some benchmark dataset. Next, the results have been compared with some of state-of-the-art approaches to prove the quality of the proposed approach.

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            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            25 February 2021
            Affiliations
            [1 ] Department of electronic engineering, Online computer vision research group
            Article
            10.14293/S2199-1006.1.SOR-.PPKLVDA.v1
            489d9446-0e2f-47b3-8a46-1e0fc3f89970

            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

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