Harald Stiegler

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Information or feedback about users' information needs beyond a brief query is crucial for improving the effectiveness of video search. One paradigm that addresses this issue is search and linking, i.e., after an initial search the user selects an item and requests a set of items that share properties with the one selected. We propose an approach for the(More)
The event synchronisation task addresses the problem of aligning photo streams from different users temporally and identifying coherent events in the streams. In our approach, we first determine the visual similarity of image pairs. We determine visual similarity based on full matching of SIFT descriptors and based on VLAD, and compare the use of the two(More)
The event synchronisation task addresses the problem of aligning media (i.e., photo and video) streams (" galleries ") from different users temporally and identifying coherent events in the streams. Our approach uses the visual similarity of image/key frame pairs based on full matching of SIFT de-scriptors with geometric verification. Based on the visual(More)
This paper describes the work done by the TOSCA-MP team for the linking subtask. We submitted three sets of runs: text-only with fixed segments, text-only aligned with shot boundaries, and text and visual with fixed segments. Each of these sets consists of six runs, using combinations of three different types of text resources and for each using only the(More)
This paper describes the work done by the JRS team for the linking sub-task. We submitted eight pairs of runs: four with different textual resources only, two using reranking based on visual similarity, and two using concept detection results. Each of the pairs contains of one run using the anchor segment only, and one using a longer context segment. The(More)
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. 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(More)
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