LORD: Tracking mobile clients in a real mesh

Bezahaf, Mehdi; Iannone, Luigi; Amorim, Marcelo Dias de; Fdida, Serge

doi:10.1016/j.adhoc.2011.03.009

Cited by 1 publication

(2 citation statements)

References 29 publications

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“…2 In this work, we use a score function, Score(R, M), to assess the identification quality, which aims to assess the uniqueness of the identification. We claim that an ROA r i accurately identifies a ground truth region m j if all photos in m j are contained in r i .…”

Section: Metric Functionsmentioning

confidence: 99%

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Travel topic analysis: a mutually reinforcing method for geo-tagged photos

Kou¹,

U²,

Yang³

et al. 2015

Geoinformatica

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Sharing personal activities on social networks is very popular nowadays, where the activities include updating status, uploading dining photos, sharing video clips, etc. Finding travel interests hidden in these vast social activities is an interesting but challenging problem. In this work, we attempt to discover travel interests based on the spatial and temporal information of geo-tagged photos. Obviously the visit sequence of a traveler can be approximately captured by her shared photos based on the timestamps and geo-locations. To extract underlying travel topics from abundant visit sequences, we study a novel mixture model to estimate the visiting probability of regions of attractions (ROAs). Such travel topics can be used in different applications, such as advertisements, promotion strategies, and city planning. To enhance the estimation result, we propose a mutual reinforcement framework to improve the quality of ROAs. Finally, we thoroughly evaluate and demonstrate our findings by the photo sharing activities collected from Flickr TM .

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Section: Metric Functionsmentioning

confidence: 99%

“…In practice, the geo-tagged data can be extracted from traveler activities in several ways, such as metro card records [29], mobile phone signals [2], etc. However, most of them are not public available due to privacy concerns.…”

Section: Introductionmentioning

confidence: 99%