Image classification approach based on statistical features of speed up robust feature set

doi:10.11772/j.issn.1001-9081.2015.01.0224

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (1): 224-230.DOI: 10.11772/j.issn.1001-9081.2015.01.0224

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Image classification approach based on statistical features of speed up robust feature set

WANG Shu¹, LYU Xueqiang¹, ZHANG Kai², LI Zhuo¹

1. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China;
2. Municipal Transportation Operations Coordination Center, Beijing Chaoyang District Municipal Commission of City Administration and Environment, Beijing 100021, China

Received:2014-07-31 Revised:2014-09-17 Online:2015-01-01 Published:2015-01-26

基于快速鲁棒特征集合统计特征的图像分类方法

王澍¹, 吕学强¹, 张凯², 李卓¹

1. 北京信息科技大学网络文化与数字传播北京市重点实验室, 北京100101;
2. 北京市朝阳区市政市容管理委员会交通运行协调指挥中心, 北京100021

通讯作者: 王澍
作者简介:王澍(1986-),男,山东昌乐人,硕士研究生,CCF会员,主要研究方向:图像标注;吕学强(1970-),男,山东鱼台人,教授,博士,CCF会员,主要研究方向:中文与多媒体信息处理;张凯(1985-),男,河南焦作人,工程师,硕士,主要研究方向:多媒体信息处理;李卓(1983-),男,河南信阳人,讲师,博士,主要研究方向:移动互联网.
基金资助:
国家自然科学基金资助项目(61271304);北京市教委科技发展计划重点项目暨北京市自然科学基金B类重点项目(KZ201311232037);北京市属高等学校创新团队建设与教师职业发展计划项目(IDHT20130519).

Abstract

Abstract:

The current method of image classification which uses the Speed Up Robust Feature (SURF) is low in efficiency and accuracy. To overcome these shortages, this paper proposed an approach for image classification which uses the statistical features of the SURF set. This approach took all dimensions and scale information of the SURF as independent random variables, and split the data with the sign of Laplace response. Firstly, the SURF vector set of the image was got. Then the feature vector was constructed with the first absolute order central moments and weighted first absolute order central moments of each dimision. Finally, the Support Vector Machine (SVM) accomplished the image classification process with this vector. The experimental results show that the precision of this approach is better than that of the methods of SURF histogram and 3-channel-Gabor texture features by increases of 17.6% and 5.4% respectively. By combining this approach with the HSV histogram, a high-level feature fusion method was got, and good classification performance was obtained. Compared with the fused method of the SURF histogram and HSV histogram, the fused method of 3-channel-Gabor texture features and HSV histogram, and the multiple-instance-learning method based on the model of Bag of Visual Word (BoVW), the fused method of this approach and HSV histogram has better precision with the increases of 5.2%, 6.8% and 3.2% respectively.

Key words: Speed Up Robust Feature (SURF), image classification, statistical feature, random variable, Support Vector Machine (SVM)

摘要：

针对现有利用快速鲁棒特征(SURF)进行图像分类的方法中存在的效率低、正确率低的问题,提出一种利用图像SURF集合的统计特征进行图像分类的方法.该方法将SURF的各个维度及尺度信息视为各自独立的随机变量,并利用拉普拉斯响应区分不同数据.首先,获取图像的SURF向量集合;然后,分维度计算SURF向量集合的一阶中心绝对矩、带权一阶中心绝对矩等统计特征,并构建特征向量;最后,结合支持向量机(SVM)进行图像分类.在Corel 1K图像库上的实验结果表明,该方法查准率较SURF直方图方法和三通道Gabor纹理特征方法分别提高17.6%和5.4%.通过与HSV直方图特征进行高级特征融合,可获得良好的分类性能.与SURF直方图结合HSV直方图方法、三通道Gabor纹理特征结合HSV直方图方法、基于视觉词袋(BoVW)模型的多示例学习方法相比,查准率分别提高了5.2%,6.8%,3.2%.

关键词: 快速鲁棒特征, 图像分类, 统计特征, 随机变量, 支持向量机

CLC Number:

TP18
TP391

WANG Shu, LYU Xueqiang, ZHANG Kai, LI Zhuo. Image classification approach based on statistical features of speed up robust feature set[J]. Journal of Computer Applications, 2015, 35(1): 224-230.

王澍, 吕学强, 张凯, 李卓. 基于快速鲁棒特征集合统计特征的图像分类方法[J]. 计算机应用, 2015, 35(1): 224-230.

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Image classification approach based on statistical features of speed up robust feature set

基于快速鲁棒特征集合统计特征的图像分类方法

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