Table of Content

Open AccessOpen Access

ARTICLE

Street-Level Landmarks Acquisition Based on SVM Classifiers

Ruixiang Li1,2, Yingying Liu3, Yaqiong Qiao1,2, Te Ma1,2, Bo Wang4, Xiangyang Luo1,2,*

State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, 450001, China.
Zhengzhou Science and Technology Institute, Zhengzhou, 450001, China.
Henan Institute of Animal Husbandry Economics, Zhengzhou, 450044, China.
State University of New York at Buffalo, New York, 14260-1660, United States.

* Corresponding Author: Xiangyang Luo. Email: .

Computers, Materials & Continua 2019, 59(2), 591-606. https://doi.org/10.32604/cmc.2019.05208

Abstract

High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence is constructed, and the IPs with unknown service are classified using the sequence. Finally, according to the domain name corresponding to the IP, the relationship between the classified server IP and organization name is established. The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially, and the median geolocation error using evaluated landmarks is reduced by about 2 km.

Keywords


Cite This Article

R. Li, Y. Liu, Y. Qiao, T. Ma, B. Wang et al., "Street-level landmarks acquisition based on svm classifiers," Computers, Materials & Continua, vol. 59, no.2, pp. 591–606, 2019.

Citations




This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1970

    View

  • 801

    Download

  • 0

    Like

Related articles

Share Link

WeChat scan