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dc.contributor.authorArandelovic, Ognjen
dc.identifier.citationArandelovic , O 2017 , ' Baseline fusion for image an pattern recognition - what not to do (and how to do better) ' , Journal of Imaging , vol. 3 , no. 4 , 44 , pp. 1-16 .
dc.identifier.otherPURE: 251245631
dc.identifier.otherPURE UUID: 57bcf6fe-b607-40fc-a220-d89080729a8c
dc.identifier.otherScopus: 85048783786
dc.identifier.otherWOS: 000424411800005
dc.description(Special issue on Computer Vision and Pattern Recognition).en
dc.description.abstractThe ever-increasing demand for a reliable inference capable of handling unpredictable challenges of practical application in the real world has made research on information fusion of major importance; indeed, this challenge is pervasive in a whole range of image understanding tasks. In the development of the most common type—score-level fusion algorithms—it is virtually universally desirable to have as a reference starting point a simple and universally sound baseline benchmark which newly developed approaches can be compared to. One of the most pervasively used methods is that of weighted linear fusion. It has cemented itself as the default off-the-shelf baseline owing to its simplicity of implementation, interpretability, and surprisingly competitive performance across a widest range of application domains and information source types. In this paper I argue that despite this track record, weighted linear fusion is not a good baseline on the grounds that there is an equally simple and interpretable alternative—namely quadratic mean-based fusion—which is theoretically more principled and which is more successful in practice. I argue the former from first principles and demonstrate the latter using a series of experiments on a diverse set of fusion problems: classification using synthetically generated data, computer vision-based object recognition, arrhythmia detection, and fatality prediction in motor vehicle accidents. On all of the aforementioned problems and in all instances, the proposed fusion approach exhibits superior performance over linear fusion, often increasing class separation by several orders of magnitude.
dc.relation.ispartofJournal of Imagingen
dc.rightsCopyright the Author 2017. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).en
dc.subjectImage matchingen
dc.subjectObject recognitionen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT Technologyen
dc.titleBaseline fusion for image an pattern recognition - what not to do (and how to do better)en
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.description.statusPeer revieweden

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