Efficient method for image classification based on low-scale bag of word model

被引：0
|
作者：
Xiao Z. [1 ]
Qin Z.-G. [1 ]
Ding Y. [1 ]
Lan T. [1 ]
Yu Y. [1 ]
机构：
[1] School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2016年 / 45卷 / 06期
关键词：
Bag-of-words; Computer vision; Image classification; Scale invariant feture transform (SIFT); Wavelet transform;
D O I：
10.3969/j.issn.1001-0548.2016.06.021
中图分类号：
学科分类号：
摘要：
This paper proposes a new framework to improve the efficiency of visual bag-of-words model in large scale image classification. The method is based on the low scale image representation obtained by wavelet transform, and the low scale visual dictionary is built by extracting the SIFT features on the low scale image. Since the feature dimension is reduced, the method can quickly generate the visual dictionary and minimize the time of image classification process. The results of comparison experiments on the 8677 images of Caltech 101 show that the proposed method can effectively improve the classification performance and efficiency of the traditional visual bag-of-words model and the Pyramid-BOW model. © 2016, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码：997 / 1001
页数：4
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