The AXES project participated in the interactive instance search task (INS), the semantic indexing task (SIN) the multimedia event recounting task (MER), and the multimedia event detection task (MED) for TRECVid 2013. Our interactive INS focused this year on using classifiers trained at query time with positive examples collected from external search engines. Participants in our INS experiments were carried out by students and researchers at Dublin City University. Our best INS runs performed on par with the top ranked INS runs in terms of P@10 and P@30, and around the median in terms of mAP.
For SIN, MED and MER, we use systems based on state- of-the-art local low-level descriptors for motion, image, and sound, as well as high-level features to capture speech and text and the visual and audio stream respectively. The low-level descriptors were aggregated by means of Fisher vectors into high- dimensional video-level signatures, the high-level features are aggregated into bag-of-word histograms. Using these features we train linear classifiers, and use early and late-fusion to combine the different features. Our MED system achieved the best score of all submitted runs in the main track, as well as in the ad-hoc track.
This paper describes in detail our INS, MER, and MED systems and the results and findings of our experiment
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Status = Published
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Subject = Engineering: Signal processing
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Subject = Computer Science: Multimedia systems
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
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DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
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Subject = Engineering
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DCU Faculties and Centres = Research Initiatives and Centres
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Publication Type = Conference or Workshop Item
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
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Subject = Computer Science: Interactive computer systems
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Subject = Computer Science: Image processing
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Subject = Computer Science
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DCU Faculties and Centres = DCU Faculties and Schools
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Subject = Computer Science: Information retrieval
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing
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Subject = Computer Science: Digital video
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Subject = Computer Science: Computer software
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Subject = Computer Science: Machine learning
Andrew Zisserman,
Heng Wang,
Jakob Verbeek,
Tinne Tuytelaars,
David Scott,
Jochen Schwenninger,
Cordelia Schmid,
Jérôme Revaud,
Danila Potapov,
Omkar M. Parkhi
and 9 others