[Zurück]

W. Bailer, S. Hölzl, R. Mörzinger, H. Stiegler, F. Lee, R. Sorschag:
"JOANNEUM RESEARCH and Vienna University of Technology at TRECVID 2010";
2010.

We participated in two tasks: semantic indexing (SIN) and
instance search (INS).
SIN runs
We submitted 4 light runs, 2 with RBF kernel, 2 with a
kernel combining appropriate kernels for the different features.
Two runs were trained on the 2010 training set, two on the
2007 training set (for the 3 concepts shared between 2007 and
2010).
L A JRS-VUT1 2: RBF kernel trained on 2010 set.
L A JRS-VUT2 1: Combined kernel trained on 2010
set.
L B JRS-VUT3 4: RBF kernel trained on 2007 set.
L B JRS-VUT4 3: Combined kernel trained on 2007
set.
The combined kernel outperforms the RBF kernel on the
2010 data. For the RBF kernel, training on 2007 data yields
worse results, for the combined kernel no clear trend can be
seen.
INS runs
All runs use the same features and differ by the method for
fusing and ranking results from these features.
F X NO JRS max max 4: For each shot, maximum
similarity of features of all query samples.
F X NO JRS topK 4: Top-k results for each feature
(k = 1000=nFeatures).
F X NO JRS w bestR 2: Weighted linear combination
of feature similarities, weights based on best ranked other
query sample.
F X NO JRS w t100 3: Weighted linear combination
of feature similarities, weights based on number of other
query samples among top 100.
Features worked best for object queries, weighted fusion
was better. For persons and objects a single feature outperformed
the best fused result, for other types fused results were
better than any single feature.