Digital image classification algorithm based on LBP and LSSVM

Gen-shan ZHANG; Jian-en TIAN; Zhe ZHANG

doi:10.3788/YJYXS20203505.0471

您当前的位置：

首页 >

文章列表页 >

Digital image classification algorithm based on LBP and LSSVM

Image Processing | 更新时间：2020-08-12

- Digital image classification algorithm based on LBP and LSSVM
- Chinese Journal of Liquid Crystals and Displays Vol. 35, Issue 5, Pages: 471-476(2020)
- 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：
- 基金信息：
- DOI：10.3788/YJYXS20203505.0471
  CLC： TP391.4;TH691.9
- Received：30 August 2019，
  
  Revised：25 September 2019，
  
  Accepted：25 September 2019，
  
  Published：05 May 2020
- 稿件说明：
移动端阅览
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：

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

A SABOORI , A TORAHIA , H R R BAKHTYARI . Combining multi-scale textural features from the panchromatic bands of high spatial resolution images with ANN and MLC classification algorithms to extract urban land uses . International Journal of Remote Sensing , 2019 . 40 ( 22 ): 8608 - 8634 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1080/01431161.2019.1620371 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1080/01431161.2019.1620371 .

M M Z ALOM , C YAKOPCIC , M S NASRIN , 等 . Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network . Journal of Digital Imaging , 2019 . 32 ( 4 ): 605 - 617 .

Y N SUN , B XUE , M J ZHANG , 等 . A particle swarm optimization-based flexible convolutional autoencoder for image classification . IEEE Transactions on Neural Networks and Learning Systems , 2019 . 30 ( 8 ): 2295 - 2309 .

黄凯奇 , 任伟强 , 谭铁牛 . 图像物体分类与检测算法综述 . 计算机学报 , 2014 . 37 ( 6 ): 1225 - 1240 . http://d.old.wanfangdata.com.cn/Periodical/jsjxb201406001 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201406001 .

K Q HUANG , W Q REN , T N TAN . A review on image object classification and detection . Chinese Journal of Computers , 2014 . 37 ( 6 ): 1225 - 1240 . http://d.old.wanfangdata.com.cn/Periodical/jsjxb201406001 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201406001 .

C Y SUEN , R LEGAULT , C NADAL , 等 . Building a new generation of handwriting recognition systems . Pattern Recognition Letters , 1993 . 14 ( 4 ): 303 - 315 . http://d.old.wanfangdata.com.cn/NSTLQK/10.1016-0167-8655(93)90096-V/ http://d.old.wanfangdata.com.cn/NSTLQK/10.1016-0167-8655(93)90096-V/ .

D MISHKIN , N SERGIEVSKIY , J MATAS . Systematic evaluation of convolution neural network advances on the Imagenet . Computer Vision and Image Understanding , 2017 . 161 11 - 19 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7c36f61aa675ab0ba4b8a88cd808d92a http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7c36f61aa675ab0ba4b8a88cd808d92a .

O RUSSAKOVSKY , J DENG , H SU , 等 . ImageNet large scale visual recognition challenge . International Journal of Computer Vision , 2015 . 115 ( 3 ): 211 - 252 . http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC0214533907/ http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC0214533907/ .

A VAILAYA , M A T FIGUEIREDO , A K JAIN , 等 . Image classification for content-based indexing . IEEE Transactions on Image Processing , 2001 . 10 ( 1 ): 117 - 130 . http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_d28ff60acb0b83140c9e3a6457f98a48 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_d28ff60acb0b83140c9e3a6457f98a48 .

A ISCEN , G TOLIAS , P H GOSSELIN , 等 . A comparison of dense region detectors for image search and fine-grained classification . IEEE Transactions on Image Processing , 2015 . 24 ( 8 ): 2369 - 2381 .

唐大伟 , 鲁东明 , 许端清 , 等 . 壁画图像分类中的分组多实例学习方法 . 中国图象图形学报 , 2014 . 19 ( 5 ): 708 - 715 . http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201405008 http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201405008 .

D W TANG , D M LU , D Q XU , 等 . Clustered multiple instance learning for mural image classification . Journal of Image and Graphics , 2014 . 19 ( 5 ): 708 - 715 . http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201405008 http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201405008 .

吴映铮 , 杨柳涛 . 基于HOG和SVM的船舶图像分类算法 . 上海船舶运输科学研究所学报 , 2019 . 42 ( 1 ): 58 - 64 . http://d.old.wanfangdata.com.cn/Periodical/jtbshcbyskxyjsxb201901012 http://d.old.wanfangdata.com.cn/Periodical/jtbshcbyskxyjsxb201901012 .

Y Z WU , L T YANG . Ship image classification by combined use of HOG and SVM . Journal of Shanghai Ship and Shipping Research Institute , 2019 . 42 ( 1 ): 58 - 64 . http://d.old.wanfangdata.com.cn/Periodical/jtbshcbyskxyjsxb201901012 http://d.old.wanfangdata.com.cn/Periodical/jtbshcbyskxyjsxb201901012 .

牟多铎 , 刘磊 . ELM与SVM在高光谱遥感图像监督分类中的比较研究 . 遥感技术与应用 , 2019 . 34 ( 1 ): 115 - 124 . http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201901012 http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201901012 .

D D MOU , L LIU . Comparative study of ELM and SVM in hyperspectral image supervision classification . Remote Sensing Technology and Application , 2019 . 34 ( 1 ): 115 - 124 . http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201901012 http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201901012 .

G J DONG , J ZHANG , M J XU , 等 . Remote sensing image classification algorithm based on Hopfield neural network . Journal of Institute of Surveying and Mapping , 2003 . 20 ( 4 ): 267 - 270, 274 .

T AHONEN , A HADID , M PIETIKAINEN . Face description with local binary patterns:application to face recognition . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2006 . 28 ( 12 ): 2037 - 2041 .

T OJALA , M PIETIKAINEN , T MAENPAA . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2002 . 24 ( 7 ): 971 - 987 .

M A HEARST , S T DUMAIS , E OSUNA , 等 . Support vector machines . IEEE Intelligent Systems and their Applications , 1998 . 13 ( 4 ): 18 - 28 . http://d.old.wanfangdata.com.cn/Periodical/kzyjc200303025 http://d.old.wanfangdata.com.cn/Periodical/kzyjc200303025 .

J A K SUYKENS , J VANDEWALLE . Chaos control using least-squares support vector machines . International Journal of Circuit Theory & Applications , 1999 . 27 ( 6 ): 605 - 615 . http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025654202/ http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025654202/ .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰