基于空间矢量模型的图像分类方法
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摘要
针对词袋模型视觉单词没有考虑空间信息的不足,提出了一种基于视觉单词间空间位置信息的空间矢量模型。该模型利用视觉单词的空间位置信息,采用图像空间矢量模型对图像进行表述,从而达到了更好的分类效果。实验在两个标准图像数据集Caltech-101和Caltech-256上进行,分别采用支持向量机(SVM)和K最近邻分类器(KNN)对其进行分类。实验表明,空间矢量模型有效地提高了平均分类正确率(ACA)和平均类别准确率(ACP),具有很好的分类效果。
A space vector model is proposed to overcome the lack of spatial location information in the bag-of-words model.The model turns visual words into vector model using the space location information of visual words to represent image,thereby achieves better classification performance.Experiments are carried out on two standard image datasets Caltech-101 and Caltech-256,respectively,with Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)classifiers.The results show that the space vector model can effectively improve the Average Classification Accuracy(ACA)and Average Category Precision(ACP),and has a good classification effect.
A space vector model is proposed to overcome the lack of spatial location information in the bag-of-words model.The model turns visual words into vector model using the space location information of visual words to represent image,thereby achieves better classification performance.Experiments are carried out on two standard image datasets Caltech-101 and Caltech-256,respectively,with Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)classifiers.The results show that the space vector model can effectively improve the Average Classification Accuracy(ACA)and Average Category Precision(ACP),and has a good classification effect.
引文
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[3]生海迪.视觉词袋模型的改进及其在图像分类中的应用研究[D].济南:山东师范大学信息科学与工程学院,2015.Sheng Hai-di.The improvement of bag-of-visual-words model and its application research in images classification[D].Ji′nan:College of Information Science and Engineering,Shandong Normal University,2015.
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[5]Wu Lei,Hu Yang,Li Ming-jing,et al.Scale-invariant visual language modeling for object categorization[J].IEEE Transactions on Multimedia,2009,11(2):286-294.
[6]Jiang Y,Wang R,Wang C.Scene classification with context pyramid features[J].Journal of Computer-Aided Design and Computer Graphics,2010,22(8):1366-1373.
[7]纪昌明,周婷,向腾飞,等.基于网格搜索和交叉验证的支持向量机在梯级水电系统隐随机调度中的应用[J].电气自动化设备,2014,34(3):125-127.Ji Chang-ming,Zhou Ting,Xiang Teng-fei,et al.Application of support vector machine based on grid search and cross validation in implicit stochastic dispatch of cascaded hydropower stations[J].Electric Power Automation Equipment,2014,34(3):125-127.
[8]闫永刚,马廷淮,王建.KNN分类算法的MapReduce并行化实现[J].南京航空航天大学学报,2013,45(4):550-555.Yan Yong-gang,Ma Ting-huai,Wang Jian.Parallel implementing KNN classification algorithm using Mapreduce programing model[J].Journal of Nanjing University of Aeronautics and Astronautics,2013,45(4):550-555.
[9]严利鑫,黄珍,朱敦尧,等.基于马尔科夫毯和隐朴素贝叶斯的驾驶行为险态辨识[J].吉林大学学报:工学版,2016,46(6):1851-1857.Yan Li-xin,Huang Zhen,Zhu Dun-yao,et al.Driving risk status identification based on Markov blanket hidden Naive Bayes[J].Journal of Jilin University(Engineering and Technology Edition),2016,46(6):1851-1857.
[10]王寅同,王建东,陈海燕.时间序列降维及机场噪声中的机型识别[J].吉林大学学报:工学版,2016,46(4):1202-1208.Wang Yan-tong,Wang Jian-dong,Chen Hai-yan.Time series dimensionality reduction and aircraft model recognition in airport-noise[J].Journal of Jilin University(Engineering and Technology Edition),2016,46(4):1202-1208.
[11]汤鹏杰,王瀚漓,左凌轩.并行交叉的深度卷积神经网络模型[J].中国图像图形学报,2016,21(3):339-347.Tang Peng-jie,Wang Han-li,Zuo Ling-xuan.Parallel cross deep convolution neural networks model[J].Journal of Image and Graphics,2016,21(3):339-347.