局部位置特征与全局轮廓特征相结合的图像分类方法

doi:10.3969/j.issn.0372-2112.2018.07.026

电子学报 ›› 2018, Vol. 46 ›› Issue (7): 1726-1731.DOI: 10.3969/j.issn.0372-2112.2018.07.026

局部位置特征与全局轮廓特征相结合的图像分类方法

李雅倩¹, 吴超², 李海滨¹, 刘彬²

1. 燕山大学工业计算机控制工程河北省重点实验室, 河北秦皇岛 066004;
2. 燕山大学信息科学与工程学院, 河北秦皇岛 066004

收稿日期:2017-01-06 修回日期:2017-12-01 出版日期:2018-07-25
- 通讯作者:
- 吴超
- 作者简介:
- 李雅倩,女.1982年4月出生,湖南岳阳人.2010年毕业于法国鲁昂应用科学学院信息学专业,现为燕山大学电气工程学院副教授,研究方向为模式识别.Email:yaqianli@126.com
- 基金资助:
- 河北省自然科学基金 (No.F2015203212）

Image Classification Method Combining Local Position Feature with Global Contour Feature

LI Ya-qian¹, WU Chao², LI Hai-bin¹, LIU Bin²

1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China;
2. Institute of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China

Received:2017-01-06 Revised:2017-12-01 Online:2018-07-25 Published:2018-07-25
- Corresponding author:
- WU Chao
- Supported by:
- National Natural Science Foundation of Hebei Province, China (No.F2015203212)

摘要/Abstract

摘要： 在空间金字塔词袋模型的基础上，针对其空间信息利用不足的问题，本文先计算图像中每一个字典向量的相对位置分布来提取出局部位置特征.然后，用非下采样轮廓波变换和线性判别分析来生成图像的全局轮廓特征.最后，通过局部位置特征与全局轮廓特征相结合的方式提高空间信息利用率，从而提高场景和物体图像分类正确率.为了检验方法的可行性，本文分别在数据库Caltech 101、MSRC和15 Scene上进行实验.实验结果证明，本文提出的方法进一步利用了空间信息，从而提高了分类正确率.

关键词: 图像分类, 词袋模型, 局部位置特征, 非下采样轮廓波变换, 全局轮廓特征

Abstract: Based on Spatial Pyramid Matching method,aiming at the insufficient utilization of spatial information,firstly,local position feature is extracted by computing relative position distribution of each dictionary vector in image.Then,global contour feature is generated through Nonsubsampled Contourlet Transform and Linear Discriminant Analysis.Finally,Spatial information is enhanced by combining local position feature with global contour feature,which consequently improves the accuracy of scene and object classification.Extensive experiments are performed on Caltech 101,MSRC and 15 Scene datasets respectively.The experimental results show that the proposed method further utilizes the spatial information,and thus improves the accuracy of image classification.

Key words: image classification, bag of words, local position feature, nonsubsampled contourlet transform, global contour feature

中图分类号:

TP391.41

李雅倩, 吴超, 李海滨, 等. 局部位置特征与全局轮廓特征相结合的图像分类方法[J]. 电子学报, 2018, 46(7): 1726-1731.

LI Ya-qian, WU Chao, LI Hai-bin, et al. Image Classification Method Combining Local Position Feature with Global Contour Feature[J]. Acta Electronica Sinica, 2018, 46(7): 1726-1731.

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局部位置特征与全局轮廓特征相结合的图像分类方法

Image Classification Method Combining Local Position Feature with Global Contour Feature

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