Combination of Texture, Color and Shape Operators to Describe Image Content: A Survey

Sana K. Ardestani

doi:10.20944/preprints202012.0479.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 15 December 2020 / Approved: 18 December 2020 / Online: 18 December 2020 (16:21:50 CET)

In many image processing and computer vision applications, the main aim is to describe image contents. So, different visual properties such as color, texture and shape are extracted to make aim. In this respect, texture information play important role in image description and visual pattern classification. Texture is referred to a specific local distribution of intensities that is repeated throughout the image. Since now different operations or descriptors have been proposed to analysis texture characteristics. In the multi object images specific texture operators usually doesn’t provide accurate results. So, in many cases, combination of texture operators are used to achieve more discriminant features. In this paper, some combination methods are survived to analysis effect of combinational texture features in image content description. Also, in the result part, different related methods are compared in terms of accuracy and computational complexity.

Image classification; Texture image analysis; Discriminant features; Combination methods; texture operators

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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Combination of Texture, Color and Shape Operators to Describe Image Content: A Survey

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