Image classification methods based on space vector model

被引：0
作者：
Chen M.-S. [1 ]
Su Y. [1 ]
Sang A.-J. [1 ]
Li P.-P. [1 ]
机构：
[1] College of Communication Engineering, Jilin University, Changchun
来源：
| 2018年 / Editorial Board of Jilin University卷 / 48期
关键词：
Bag-of-words model; Image classification; Information processing technology; Space vector model; Vector matrix;
D O I：
10.13229/j.cnki.jdxbgxb20170444
中图分类号：
学科分类号：
摘要：
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. © 2018, Editorial Board of Jilin University. All right reserved.
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页码：943 / 951
页数：8
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