Image classification using local binary pattern operators for static images

Oana Astrid Vatamanu, Mircea Jivulescu
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引用次数: 10

Abstract

This paper aims to present an image classification method using Local Binary Pattern techniques. Local Binary Pattern operator transforms an static image, at pixel level, into a matrix of labels. These labels - integer numbers - describe and characterise the original image at a much lower scale. The authors propose the use of labels as a global characteristic of an static image. These techniques can be applied to an image or to a group of images and the characterization is done through an array of values extracted by the algorithm. The application developed allows the characterization of an image or a set of images, determining the similarity between different images and the degree of belonging to a particular group. Vectors of values are required for more images and image groups and each vector is representing different textures and their classification. As a result it becomes possible that indexing images, take into account the content of the information present in the image.
使用局部二值模式算子对静态图像进行图像分类
提出了一种基于局部二值模式的图像分类方法。局部二值模式算子在像素级将静态图像转换为标签矩阵。这些标签——整数——在一个低得多的尺度上描述和表征原始图像。作者建议使用标签作为静态图像的全局特征。这些技术可以应用于一张图像或一组图像,通过算法提取的一组值来完成特征描述。所开发的应用程序允许对图像或一组图像进行表征,确定不同图像之间的相似性以及属于特定组的程度。更多的图像和图像组需要值向量,每个向量代表不同的纹理及其分类。因此,索引图像,考虑到图像中存在的信息的内容成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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