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Wissenschaftliche Publikation

JOANNEUM RESEARCH and Vienna University of Technology at TRECVID 2010

Publikation aus Digital

Bailer W., Sorschag R., Mözinger R., Hölzl S., Lee F., Stiegler H.

Gaithersburg, MD, USA Proceedings of TRECVID Workshop, 11/2010


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

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