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DC Field | Value | Language |
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dc.contributor.author | Bouma, Henri | - |
dc.contributor.author | Azzopardi, George | - |
dc.contributor.author | Spitters, Martijn | - |
dc.contributor.author | Wit, Joost de | - |
dc.contributor.author | Versloot, Corne | - |
dc.contributor.author | Zon, Remco van der | - |
dc.contributor.author | Eendebak, Pieter T. | - |
dc.contributor.author | Baan, Jan | - |
dc.contributor.author | Hove, Johan-Martijn ten | - |
dc.contributor.author | Eekeren, Adam van | - |
dc.contributor.author | Haar, Frank ter | - |
dc.contributor.author | Hollander, Richard den | - |
dc.contributor.author | Huis, Jasper van | - |
dc.contributor.author | Boer, Maaike de | - |
dc.contributor.author | Antwerpen, Gert van | - |
dc.contributor.author | Broekhuijsen, Jeroen | - |
dc.contributor.author | Daniele, Laura | - |
dc.contributor.author | Brandt, Paul | - |
dc.contributor.author | Schavemaker, John | - |
dc.contributor.author | Kraaij, Wessel | - |
dc.contributor.author | Schutte, Klamer | - |
dc.date.accessioned | 2018-02-02T07:24:42Z | - |
dc.date.available | 2018-02-02T07:24:42Z | - |
dc.date.issued | 2013-11 | - |
dc.identifier.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. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/26328 | - |
dc.description.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. | en_GB |
dc.description.sponsorship | This research for the MED submission was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. The INS submission was partly supported by the MIME project of the creative industries knowledge and innovation network CLICKNL. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | TRECVID | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Computer vision | en_GB |
dc.subject | Support vector machines | en_GB |
dc.subject | Multimedia systems | en_GB |
dc.title | TNO at TRECVID 2013 : multimedia event detection and instance search | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | TRECVID 2013 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
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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