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Detecting tourist attractions using geo-tagged photo clustering

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成果类型:
期刊论文
作者:
Wenyuan Zhang*;Guoxin Tan(谈国新);Ming Lei;Xiaomei Guo;Chuanming Sun
通讯作者:
Wenyuan Zhang
作者机构:
[Guoxin Tan; Wenyuan Zhang; Ming Lei; Xiaomei Guo; Chuanming Sun] Central China Normal University, Wuhan, China
通讯机构:
[Wenyuan Zhang] C
Central China Normal University, Wuhan, China
语种:
英文
关键词:
density-based clustering;Flickr;geo-tagged photo;tourist attraction
期刊:
Chinese Sociological Dialogue
ISSN:
2397-2009
年:
2018
卷:
3
期:
1
页码:
3 - 16
基金类别:
The National Social Science Fund of China#&#&#13AZD008
机构署名:
本校为第一且通讯机构
摘要:
Millions of geo-tagged photos are becoming available due to the wide spread of photo-sharing websites, which provide valuable information to mine spatial patterns from human activities. In this study, we present a simple and fast density-based spatial clustering algorithm to detect popular scenic spots using geo-tagged photos collected from Flickr. In this algorithm, Gaussian kernel is applied to estimate local density of data points, and a decision graph is used to obtain cluster centers easily. More than 289,000 geo-tagged photos located in five typical cities of China are downloaded as cas...

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