A utility model for photo selection in mobile crowdsensing

Tongqing Zhou, Bin Xiao, Zhiping Cai, Ming Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)


Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet, another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos' spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of $33\%$33% and $18.7\%$18.7% on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8 percent improvement on the photo coverage performance.

Original languageEnglish
Article number8840972
Pages (from-to)48-62
Number of pages15
JournalIEEE Transactions on Mobile Computing
Issue number1
Publication statusPublished - 1 Jan 2021


  • mobile crowdsensing
  • photo coverage
  • photo selection
  • ubiquitous computing
  • view quality

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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