Image classification based on feature selection and SVMs

doi:10.3778/j.issn.1002-8331.2010.05.052

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (5): 169-172.DOI: 10.3778/j.issn.1002-8331.2010.05.052

• 图形、图像、模式识别 • Previous Articles Next Articles

Image classification based on feature selection and SVMs

GAO Yong-gang¹，ZHOU Ming-quan²，GENG Guo-hua¹，LIU Yan-wu¹

1.Department of Information Science & Technology，Northwest University，Xi’an 710127，China
2.Department of Information Science & Technology，Beijing Normal University，Beijing 100875，China

Received:2008-08-25 Revised:2008-11-17 Online:2010-02-11 Published:2010-02-11
Contact: GAO Yong-gang

基于特征选择和SVMs的图像分类

高永岗¹，周明全²，耿国华¹，刘燕武¹

1.西北大学信息科学与技术学院，西安 710127
2.北京师范大学信息科学与技术学院，北京 100875

通讯作者: 高永岗

Abstract

Abstract: This paper focuses on the MI-based image feature selection method[1]，briefly describes SVMs category principle and design[2].The paper proposes the MI greedy optimal algorithm，transforms high-dimensional data processing into a one-dimensional data processing，simplifies the difficulty of the operation and increases the speed and accuracy of classification.The results show that，the eight classification，more than 1，000 classified images are better dealt with by the MI greedy optimal algorithm than the traditional classification algorithm.

Key words: feature selection, MI greedy optimal algorithm, Support Vector Machines（SVMs）

摘要： 重点论述了基于MI图像特征选择方法[1]，简要地讲述了支持向量机的SVMs分类器原理和设计[2]。提出了MI贪婪最优算法，将高维数据处理转化为一维数据处理，简化了运算难度，同时提高了分类速度和准确性。实验结果表明，通过对8个分类、上千张图片进行分类处理，效果好于传统的分类算法。

关键词: 特征选择, MI贪婪最优算法, 支持向量机（SVMs）

CLC Number:

TP391

GAO Yong-gang¹，ZHOU Ming-quan²，GENG Guo-hua¹，LIU Yan-wu¹. Image classification based on feature selection and SVMs[J]. Computer Engineering and Applications, 2010, 46(5): 169-172.

高永岗¹，周明全²，耿国华¹，刘燕武¹. 基于特征选择和SVMs的图像分类[J]. 计算机工程与应用, 2010, 46(5): 169-172.

[1]	LI Li, JI Xinyuan, SONG Song. Prediction Model for Number of Software Defects in Loop [J]. Computer Engineering and Applications, 2021, 57(7): 158-163.
[2]	LI Jingxing, YANG Youlong. Feature Selection of Markov Blanket for High Dimensional Data [J]. Computer Engineering and Applications, 2021, 57(6): 58-66.
[3]	LIN Weixing, WANG Yujia, CHEN Wanfen, LIANG Haina. High-Dimensional Data Feature Selection Algorithm Based on Multifactor Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(22): 199-207.
[4]	LI Longzhu, LIN Yaojin, LYU Yan, LU Shun, WANG Chenxi. Online Streaming Feature Selection Algorithm Using Neighborhood Information Interaction [J]. Computer Engineering and Applications, 2021, 57(21): 102-108.
[5]	CHEN Qianru, LI Yali, XU Kequan, LIU Yilong, WANG Shuqin. WKNN Feature Selection Method Based on Self-Tuning Adaptive Genetic Algorithm [J]. Computer Engineering and Applications, 2021, 57(20): 164-171.
[6]	WU Weijie, ZHANG Jingxiang. Random Forest Feature Selection Algorithm Based on Categorization Information and Application [J]. Computer Engineering and Applications, 2021, 57(17): 147-156.
[7]	QIU Yunfei, GAO Huacong. Hybrid Filter and Improved Adaptive GA for Feature Selection [J]. Computer Engineering and Applications, 2021, 57(11): 95-102.
[8]	HUO Lin, LU Yinli. Improved Particle Swarm Optimization for Android Malware Detection [J]. Computer Engineering and Applications, 2020, 56(7): 96-101.
[9]	LIAO Wenxiong, ZENG Bi, LIANG Tiankai, XU Yayun, ZHAO Junfeng. Personal Credit Risk Assessment Method for High-Dimensional Data [J]. Computer Engineering and Applications, 2020, 56(4): 219-224.
[10]	PENG Ming, ZHANG Haipeng. Unsupervised Feature Selection via Schatten-p Norm and Feature Self-Representation [J]. Computer Engineering and Applications, 2020, 56(23): 45-52.
[11]	LIU Feng, Godfred Kim Mensah, LI Xinyun, LIU Hongli, LI Yao, GUO Hao. Abnormal Topological Analysis and Classification Research of Uncertain Brain Networks [J]. Computer Engineering and Applications, 2020, 56(2): 127-132.
[12]	YUE Peng, HOU Lingyan, YANG Dali, TONG Qiang. XLC-Stacking Method for Disease Diagnosis Based on XGBoost Feature Selection [J]. Computer Engineering and Applications, 2020, 56(17): 136-141.
[13]	HUANG Xin, MO Haimiao, ZHAO Zhigang, ZENG Min. Research on Discrete Enhanced Fireworks Algorithm and [kNN] in Feature Selection [J]. Computer Engineering and Applications, 2020, 56(16): 112-117.
[14]	ZHOU Wanying, MA Yingcang, XU Qiuxia, ZHENG Yi. Unsupervised Feature Selection Algorithm Based on Maximum Entropy and [l2,0] Norm Constraints [J]. Computer Engineering and Applications, 2020, 56(11): 51-59.
[15]	CHEN Jia, LIU Dongxue, WU Dashuo. Stock Index Forecasting Method Based on Feature Selection and LSTM Model [J]. Computer Engineering and Applications, 2019, 55(6): 108-112.

Image classification based on feature selection and SVMs

基于特征选择和SVMs的图像分类

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics