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K-Space at TRECVid 2007
Citation key 1107Wilkins2007
Author P. Wilkins and T. Adamek and D. Byrne and G. J.F.Jones and H. Lee and G. Keenan and K. McGuinness and N. E. O’Connor and A. F. Smeaton and A. Amin and Z. Obrenovic and R. Benmokhtar and E. Galmar and B. Huet and S. Essid and R. Landais and F. Vallet and G. Th. Papadopoulos and S. Vrochidis and V. Mezaris and I. Kompatsiaris and E. Spyrou and Y. Avrithis and R. Mörzinger and P. Schallauer and W. Bailer and T. Piatrik and K. Chandramouli and E. Izquierdo and Martin Haller and Lutz Goldmann and Amjad Samour and Andreas Cobet and Thomas Sikora and P. Praks
Title of Book Proceedings of the TRECVid Workshop
Pages 205–216
Year 2007
Month nov
Abstract In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features.
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