.png){kind=icon}
返回
A Semi-Supervised Method for Surveillance-Based Visual Location Recognition
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2016.2578639.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we are devoted to solving the problem of crossing surveillance and mobile phone visual location recognition, especially for the case that the query and reference images are captured by mobile phone and surveillance camera, respectively. Besides, we also study the influence of the environmental condition variations on this problem. To explore that problem, we first build a cross-device location recognition dataset, which includes images of 22 locations taken by mobile phones and surveillance cameras under different time and weather conditions. Then based on careful analysis of the problems existing in the data, we specifically design a method which unifies an unsupervised subspace alignment method and the semi-supervised Laplacian support vector machine. Experiments are performed on our dataset. Compared with several related methods, our method shows to be more efficient on the problem of crossing surveillance and mobile phone visual location recognition. Furthermore, the influence of several factors such as feature, time, and weather is studied.
Keyword:
Cross-device (C-D) recognition
semi-supervised learning
visual localization
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
分享收藏Structure of thiocanate salt of 1,8-bis(dimethylamino)naphthalene (dmanH+.SCN−) at 188 and 290 K
PREAI
分享收藏
分享收藏
查看更多内容