基于LBP与LSSVM的数字图像分类算法

张艮山; 田建恩; 张哲

doi:10.3788/YJYXS20203505.0471

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基于LBP与LSSVM的数字图像分类算法

图像处理 | 更新时间：2020-08-12

- 基于LBP与LSSVM的数字图像分类算法
- Digital image classification algorithm based on LBP and LSSVM
- 液晶与显示 2020年35卷第5期页码：471-476
- 作者机构：
  
  石家庄学院教育技术中心, 河北石家庄 050035
- 作者简介：
  
  [ "张艮山(1980-), 男, 河北藁城人, 硕士, 副教授, 2006年于河北大学获得硕士学位, 主要从事数字化资源开发方面的研究。E-mail:silverhill@126.com" ]
- 基金信息：
- DOI：10.3788/YJYXS20203505.0471
  中图分类号： TP391.4;TH691.9
- 收稿日期：2019-08-30，
  
  修回日期：2019-09-25，
  
  录用日期：2019-9-25，
  
  纸质出版日期：2020-05-05
- 稿件说明：
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张艮山, 田建恩, 张哲. 基于LBP与LSSVM的数字图像分类算法[J]. 液晶与显示, 2020,35(5):471-476.

Gen-shan ZHANG, Jian-en TIAN, Zhe ZHANG. Digital image classification algorithm based on LBP and LSSVM[J]. Chinese journal of liquid crystals and displays, 2020, 35(5): 471-476.
张艮山, 田建恩, 张哲. 基于LBP与LSSVM的数字图像分类算法[J]. 液晶与显示, 2020,35(5):471-476. DOI： 10.3788/YJYXS20203505.0471.

Gen-shan ZHANG, Jian-en TIAN, Zhe ZHANG. Digital image classification algorithm based on LBP and LSSVM[J]. Chinese journal of liquid crystals and displays, 2020, 35(5): 471-476. DOI： 10.3788/YJYXS20203505.0471.

摘要

针对数字图像的高精度分类问题，提出了一种新型数字图像分类算法。在该算法中，局部二值模式（Local Binary Patterns，LBP）算子被用于数字图像的LBP图构建LBP图的直方图被用于构建图像样本的特征向量；大量样本的特征向量构建的训练数据集被送入最小二乘支持向量机（Least Squares Support Vector Machines，LSSVM）进行最优分类模型的构建。在测试数据集的分类测试中，对本文所提出算法与传统支持向量机算法、极限学习机算法和Hopfield神经网络方法进行了比较，在宏查准率、宏查全率和分类时间几个典型性能指标的测试方面，本文所提出的LBP-LSSVM算法均表现出了优异的性能。

Abstract

Aiming at the high-precision classification problem of digital images

a new digital image classification algorithm is proposed. In this algorithm

the local binary patterns (LBP) operator is used to construct the LBP image of the digital image

and then the histogram of the LBP graph is used to construct the feature vector of the image sample. Finally

the training data set constructed by the eigenvectors of a large number of samples is sent to the least squares support vector machines (LSSVM) for the construction of the classification model. In the classification test of test data sets

the proposed algorithm is compared with traditional support vector machine algorithm

extreme learning machine algorithm and Hopfield neural network method. The LBP-LSSVM algorithm presented shows excellent performance in the testing of typical performance indicators such as macro-precision rate

macro-recall rate and classification time.

关键词

Keywords

references

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