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Lookup NU author(s): Dr Aftab Khan
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A key question in machine perception is how to adaptively build upon existing capabilities so as to permit novel functionalities. Implicit in this are the notions of anomaly detection and learning transfer. A perceptual system must firstly determine at what point the existing learned model ceases to apply, and secondly, what aspects of the existing model can be brought to bear on the newlydefined learning domain. Anomalies must thus be distinguished from mere outliers, i.e. cases in which the learned model has failed to produce a clear response; it is also necessary to distinguish novel (but meaningful) input from misclassification error within the existing models. We thus apply a methodology of anomaly detection based on comparing the outputs of strong and weak classifiers to the problem of detecting the rule-incongruence involved in the transition from singles to doubles tennis videos. We then demonstrate how the detected anomalies can be used to transfer learning from one (initially known) rule-governed structure to another. Our ultimate aim, building on existing annotation technology, is to construct an adaptive system for court-based sport video annotation.
Author(s): Almajai I, Yan F, de Campos T, Khan A, Christmas W, Windridge D, Kittler J
Publication type: Book Chapter
Publication status: Published
Book Title: Detection and Identification of Rare Audiovisual Cues
Year: 2012
Volume: 384
Pages: 109-117
Series Title: Studies in Computational Intelligence
Publisher: Springer
Place Published: Berlin
URL: http://dx.doi.org/10.1007/978-3-642-24034-8_9
DOI: 10.1007/978-3-642-24034-8_9
Library holdings: Search Newcastle University Library for this item
ISBN: 9783642240348