|dc.contributor.author||Seneviratne, Attgalage Lasantha Gunathilaka||
|dc.description.abstract||Image annotation is a difficult task to achieve in an automated way.
In this thesis, a human-augmented approach to tackle this problem is discussed and
suitable strategies are derived to solve it. The proposed technique is inspired by
human-based computation in what is called “human-augmented” processing to
overcome limitations of fully automated technology for closing the semantic gap.
The approach aims to exploit what millions of individual gamers are keen to do, i.e.
enjoy computer games, while annotating media.
In this thesis, the image annotation problem is tackled by a game based
framework. This approach combines image processing and a game theoretic model
to gather media annotations. Although the proposed model behaves similar to a
single player game model, the underlying approach has been designed based on a
two-player model which exploits the player’s contribution to the game and
previously recorded players to improve annotations accuracy. In addition, the
proposed framework is designed to predict the player’s intention through
Markovian and Sequential Sampling inferences in order to detect cheating and
improve annotation performances. Finally, the proposed techniques are
comprehensively evaluated with three different image datasets and selected
representative results are reported.||en_US
|dc.publisher||Queen Mary University of London||
|dc.title||A game-based approach towards human augmented image annotation.||en_US
|dc.rights.holder||The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author||