Name
Abstract | ||
|---|---|---|
Sharing geo-tagged photos has been a hot social activity in the daily life because these photos not only contain geo information but also indicate people's hobbies, intention and mobility patterns. However, the present raw geo-tagged photo routes cannot provide information as enough as complete GPS trajectories due to the defects hidden in them. This paper mainly aims at analyzing the large amounts of geo-tagged photos and proposing a novel travel route restoring method. In our approach we first propose an Interest Measure Ratio to rank the hot spots based on density-based spatial clustering arithmetic. Then we apply the Hidden Semi-Markov model and Mean Value method to demonstrate migration discipline in the hot spots and restore the significant region sequence into complete GPS trajectory. At the end of the paper, a novel experiment method is designed to demonstrate that the approach is feasible in restoring route, and there is a good performance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3837/tiis.2013.05.017 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
Geo-tagged Photo,Hot spot,Travel route,Route restoring,Trajectory mining | Data mining,Hot spot (veterinary medicine),Mean value,Markov model,Computer science,Social activity,Simulation,Gps trajectory,Global Positioning System,Cluster analysis,Distributed computing | Journal |
Volume | Issue | ISSN |
7 | SP5 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guannan Wang | 1 | 0 | 0.34 |
| Zhizhong Wang | 2 | 49 | 6.30 |
| Zhen-Min Zhu | 3 | 201 | 21.00 |
| Saiping Wen | 4 | 0 | 0.34 |