JOANNEUM RESEARCH and Vienna University of Technology at TRECVID 2010
By Werner Bailer, Stefan Hölzl, Roland Mörzinger, Harald Stiegler, Felix Lee, and Robert Sorschag
Abstract
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.
Reference
W. Bailer, S. Hölzl, R. Mörzinger, H. Stiegler, F. Lee, R. Sorschag: "JOANNEUM RESEARCH and Vienna University of Technology at TRECVID 2010"; 2010.
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