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Title: | TNO at TRECVID 2013 : multimedia event detection and instance search |
Authors: | Bouma, Henri Azzopardi, George Spitters, Martijn Wit, Joost de Versloot, Corne Zon, Remco van der Eendebak, Pieter T. Baan, Jan Hove, Johan-Martijn ten Eekeren, Adam van Haar, Frank ter Hollander, Richard den Huis, Jasper van Boer, Maaike de Antwerpen, Gert van Broekhuijsen, Jeroen Daniele, Laura Brandt, Paul Schavemaker, John Kraaij, Wessel Schutte, Klamer |
Keywords: | Computer vision Support vector machines Multimedia systems |
Issue Date: | 2013-11 |
Publisher: | TRECVID |
Citation: | Bouma, H., Azzopardi., G., Spitters, M., De Wit, J., Versloot, C., Van der Zon, R.,...Schutte, K. (2013). TNO at TRECVID 2013 : multimedia event detection and instance search. TRECVID 2013. 1-12. |
Abstract: | We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level concepts. Automatic speech recognition (ASR) and optical character recognition (OCR) are not used in the system. In the MED case with 100 example training videos, support-vector machines (SVM) are trained and fused to detect an event in the test set. In the case with 0 example videos, positive and negative concepts are extracted as keywords from the textual event description and events are detected with the high-level concepts. The MED results show that the SIFT keypoint descriptor is the one which contributes best to the results, fusion of multiple low-level features helps to improve the performance, and the textual event-description chain currently performs poorly. The TNO INS system presents a baseline open-source approach using standard SIFT keypoint detection and exhaustive matching. In order to speed up search times for queries a basic map-reduce scheme is presented to be used on a multi-node cluster. Our INS results show above-median results with acceptable search times. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/26328 |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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ConferencePaper_TNO at TRECVID 2013_Multimedia event detection and instance search.pdf | 435.37 kB | Adobe PDF | View/Open |
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