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Open AccessArticle

Improving Search Quality in Crowdsourced Bib Number Tagging Systems Using Data Fusion

St.Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, St. Petersburg 199178, Russia
Information 2020, 11(8), 385; https://doi.org/10.3390/info11080385
Received: 11 July 2020 / Revised: 25 July 2020 / Accepted: 31 July 2020 / Published: 5 August 2020
(This article belongs to the Section Information Systems)
Today, crowd computing is successfully applied for many information processing problems in a variety of domains. One of the most acute issues with crowd-powered systems is the quality of results (as humans can make errors). Therefore, a number of methods have been proposed to process the results obtained from the crowd in order to compensate human errors. Most of the existing methods of processing contributions are constructed based on a (natural) assumption that the only information available is unreliable data obtained from the crowd. However, in some cases, additional information is available, and it can be utilized in order to improve the overall quality of the result. The paper describes a crowd computing application for community tagging of running race photos. It presents a utility analysis to identify situations in which community photo tagging is a reasonable choice. It also proposes a data fusion model making use of runners’ location information recorded in their Global Positioning System (GPS) tracks. Field experiments with the applications show that community-based tagging can collect enough contributors to process photosets from medium-sized running events. Simulation results confirm, that the use of data fusion in processing the results of crowd computing is a promising technique, and the use of probabilistic graphical models (e.g., Bayesian networks) for data fusion allows one to smoothly increase the quality of the results with an increase of the available information. View Full-Text
Keywords: crowd computing; crowdsourcing; image processing; location-based services; data fusion; human-machine systems; image tagging crowd computing; crowdsourcing; image processing; location-based services; data fusion; human-machine systems; image tagging
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Ponomarev, A. Improving Search Quality in Crowdsourced Bib Number Tagging Systems Using Data Fusion. Information 2020, 11, 385.

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