Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10322139 | Expert Systems with Applications | 2014 | 13 Pages |
Abstract
Geo-tagged photos leave trails of movement that form trajectories. Regions-of-interest detection identifies interesting hot spots where many trajectories visit and large geo-tagged photos are uploaded. Extraction of exact shapes of regions-of-interest is a key step to understanding these trajectories and mining sequential trajectory patterns. This article introduces an efficient and effective grid-based regions-of-interest detection method that is linear to the number of grid cells, and is able to detect arbitrary shapes of regions-of-interest. The proposed algorithm is combined with sequential pattern mining to reveal sequential trajectory patterns. Experimental results reveal quality regions-of-interest and promising sequential trajectory patterns that demonstrate the benefits of our algorithm.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Guochen Cai, Chihiro Hio, Luke Bermingham, Kyungmi Lee, Ickjai Lee,