K-Space at TRECVid 2007
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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
|Title of Book
||Proceedings of the TRECVid Workshop
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