Discovering areas of interest with geo-tagged images and check-ins

Liu, Jiajun, Huang, Zi, Chen, Lei, Shen, Heng Tao and Yan, Zhixian (2012). Discovering areas of interest with geo-tagged images and check-ins. In: Proceedings of the 20th ACM International Conference on Multimedia (MM'12). 20th ACM International Conference on Multimedia (MM'12), Nara, Japan, (589-598). 29 October - 2 November 2012. doi:10.1145/2393347.2393429

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Author Liu, Jiajun
Huang, Zi
Chen, Lei
Shen, Heng Tao
Yan, Zhixian
Title of paper Discovering areas of interest with geo-tagged images and check-ins
Conference name 20th ACM International Conference on Multimedia (MM'12)
Conference location Nara, Japan
Conference dates 29 October - 2 November 2012
Proceedings title Proceedings of the 20th ACM International Conference on Multimedia (MM'12)
Journal name MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
Place of Publication New York, United States
Publisher ACM
Publication Year 2012
Sub-type Fully published paper
DOI 10.1145/2393347.2393429
Open Access Status DOI
ISBN 9781450310895
Start page 589
End page 598
Total pages 10
Language eng
Abstract/Summary Geo-tagged image is an ideal source for the discovery of popular travel places. However, the aspects of popular venues for daily-life purposes like dining and shopping are often missing in the mined locations from geo-tagged images. Fortunately check-in websites provide us a unique opportunity of analyzing people's preferences in their daily lives to complement the knowledge mined from geo-tagged images. This paper presents a novel approach for the discovery of Areas of Interest (AoI). By analyzing both geo-tagged images and check-ins, the approach exploits travelers' flavors as well as the preferences of daily-life activities of local residents to find AoI in a city. The proposed approach consists of two major steps. Firstly, we devise a density-based clustering method to discover AoI, mainly based on the image densities but also reinforced by the secondary densities from the images' neighboring venues. Then we propose a novel joint authority analysis framework to rank AoI. The framework simultaneously considers both the location-location transitions, and the user-location relations. An interactive presentation interface for visualizing AoI is also presented. The approach is tested with very large datasets for Shanghai city. They consist of 49,460 geo-tagged images from Panoramio.com, and 1,361,547 check-ins from the check-in website Qieke.com. By evaluating the ranking accuracy and quality of AoI, we demonstrate great improvements of our method over compared methods.
Keyword Geo-tagged image
Check-in data
Areas of interest
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Thu, 04 Apr 2013, 03:34:32 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering