Browse DORAS
Browse Theses
Search
Latest Additions
Creative Commons License
Except where otherwise noted, content on this site is licensed for use under a:

The AXES submissions at TrecVid 2013

Aly, Robin and Arandjelovic, Relja and Chatfield, Ken and Douze, Matthijs and Fernando, Basura and Harchaoui, Zaid and McGuinness, Kevin and O'Connor, Noel E. and Oneata, Dan and Parkhi, Omkar M. and Potapov, Danila and Revaud, Jérôme and Schmid, Cordelia and Schwenninger, Jochen and Scott, David and Tuytelaars, Tinne and Verbeek, Jakob and Wang, Heng and Zisserman, Andrew (2013) The AXES submissions at TrecVid 2013. In: TRECVid 2013, 20-22 Nov 2013, Gaithersburg, Maryland, USA.

Full text available as:

[img]
Preview
PDF (Workshop paper) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
4Mb

Abstract

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

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:No
Subjects:Computer Science > Interactive computer systems
Computer Science > Machine learning
Engineering > Signal processing
Computer Science > Multimedia systems
Computer Science > Image processing
Computer Science > Computer software
Computer Science > Digital video
Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Framework Programme 7
ID Code:19671
Deposited On:26 Nov 2013 10:06 by Dr. Kevin McGuinness. Last Modified 16 Feb 2017 12:19

Download statistics

Archive Staff Only: edit this record