SoMIAP: Social Media Images Analysis and Prediction Framework

Yonghao Shi; Gueltoum Bendiab; Stavros Shiaeles; Nick Savage

doi:10.1007/978-3-030-65726-0_19

SoMIAP: Social Media Images Analysis and Prediction Framework

Yonghao Shi, Gueltoum Bendiab, Stavros Shiaeles, Nick Savage

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

The personal photos captured and submitted by users on social networks can provide several interesting insights about the user’s location; which is a key indicator of their daily activities. This information is invaluable for security organisations, especially for security monitoring and tracking criminal activities. Hence, we propose in this paper a novel approach for location prediction based on the image analysis of the photos posted on social media. Our approach combines two main methods to perform the image analysis; place and face recognition. The first method is used to determine the location area in the analysed image. The second is used to identify people in the analysed image, by locating a face in the image and comparing it with a dataset of images that have been collected from different social platforms. The effectiveness of the proposed approach is demonstrated through performance analysis and experimental results.

Original language	English
Title of host publication	Internet of Things, Smart Spaces, and Next Generation Networks and Systems
Subtitle of host publication	20th International Conference, NEW2AN 2020, and 13th Conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I
Editors	Olga Galinina, Sergey Andreev, Sergey Balandin, Yevgeni Koucheryavy
Publisher	Springer
Pages	205-216
ISBN (Electronic)	9783030657260
ISBN (Print)	9783030657253
DOIs	https://doi.org/10.1007/978-3-030-65726-0_19
Publication status	Published - 22 Dec 2020

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12525
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-65726-0_19

SoMIAP AAM
This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-65726-0_19.
Accepted author manuscript (Post-print), 1.98 MB

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Shi, Y., Bendiab, G., Shiaeles, S., & Savage, N. (2020). SoMIAP: Social Media Images Analysis and Prediction Framework. In O. Galinina, S. Andreev, S. Balandin, & Y. Koucheryavy (Eds.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 20th International Conference, NEW2AN 2020, and 13th Conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I (pp. 205-216). (Lecture Notes in Computer Science; Vol. 12525). Springer. https://doi.org/10.1007/978-3-030-65726-0_19

Shi, Yonghao ; Bendiab, Gueltoum ; Shiaeles, Stavros et al. / SoMIAP: Social Media Images Analysis and Prediction Framework. Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 20th International Conference, NEW2AN 2020, and 13th Conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I. editor / Olga Galinina ; Sergey Andreev ; Sergey Balandin ; Yevgeni Koucheryavy. Springer, 2020. pp. 205-216 (Lecture Notes in Computer Science).

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title = "SoMIAP: Social Media Images Analysis and Prediction Framework",

abstract = "The personal photos captured and submitted by users on social networks can provide several interesting insights about the user{\textquoteright}s location; which is a key indicator of their daily activities. This information is invaluable for security organisations, especially for security monitoring and tracking criminal activities. Hence, we propose in this paper a novel approach for location prediction based on the image analysis of the photos posted on social media. Our approach combines two main methods to perform the image analysis; place and face recognition. The first method is used to determine the location area in the analysed image. The second is used to identify people in the analysed image, by locating a face in the image and comparing it with a dataset of images that have been collected from different social platforms. The effectiveness of the proposed approach is demonstrated through performance analysis and experimental results.",

author = "Yonghao Shi and Gueltoum Bendiab and Stavros Shiaeles and Nick Savage",

year = "2020",

month = dec,

day = "22",

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language = "English",

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Shi, Y, Bendiab, G, Shiaeles, S & Savage, N 2020, SoMIAP: Social Media Images Analysis and Prediction Framework. in O Galinina, S Andreev, S Balandin & Y Koucheryavy (eds), Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 20th International Conference, NEW2AN 2020, and 13th Conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I. Lecture Notes in Computer Science, vol. 12525, Springer, pp. 205-216. https://doi.org/10.1007/978-3-030-65726-0_19

SoMIAP: Social Media Images Analysis and Prediction Framework. / Shi, Yonghao; Bendiab, Gueltoum; Shiaeles, Stavros et al.
Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 20th International Conference, NEW2AN 2020, and 13th Conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I. ed. / Olga Galinina; Sergey Andreev; Sergey Balandin; Yevgeni Koucheryavy. Springer, 2020. p. 205-216 (Lecture Notes in Computer Science; Vol. 12525).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - SoMIAP: Social Media Images Analysis and Prediction Framework

AU - Shi, Yonghao

AU - Bendiab, Gueltoum

AU - Shiaeles, Stavros

AU - Savage, Nick

PY - 2020/12/22

Y1 - 2020/12/22

N2 - The personal photos captured and submitted by users on social networks can provide several interesting insights about the user’s location; which is a key indicator of their daily activities. This information is invaluable for security organisations, especially for security monitoring and tracking criminal activities. Hence, we propose in this paper a novel approach for location prediction based on the image analysis of the photos posted on social media. Our approach combines two main methods to perform the image analysis; place and face recognition. The first method is used to determine the location area in the analysed image. The second is used to identify people in the analysed image, by locating a face in the image and comparing it with a dataset of images that have been collected from different social platforms. The effectiveness of the proposed approach is demonstrated through performance analysis and experimental results.

AB - The personal photos captured and submitted by users on social networks can provide several interesting insights about the user’s location; which is a key indicator of their daily activities. This information is invaluable for security organisations, especially for security monitoring and tracking criminal activities. Hence, we propose in this paper a novel approach for location prediction based on the image analysis of the photos posted on social media. Our approach combines two main methods to perform the image analysis; place and face recognition. The first method is used to determine the location area in the analysed image. The second is used to identify people in the analysed image, by locating a face in the image and comparing it with a dataset of images that have been collected from different social platforms. The effectiveness of the proposed approach is demonstrated through performance analysis and experimental results.

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T3 - Lecture Notes in Computer Science

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BT - Internet of Things, Smart Spaces, and Next Generation Networks and Systems

A2 - Galinina, Olga

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Shi Y, Bendiab G, Shiaeles S, Savage N. SoMIAP: Social Media Images Analysis and Prediction Framework. In Galinina O, Andreev S, Balandin S, Koucheryavy Y, editors, Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 20th International Conference, NEW2AN 2020, and 13th Conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I. Springer. 2020. p. 205-216. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-65726-0_19

SoMIAP: Social Media Images Analysis and Prediction Framework

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