Knowledge adaptation with partially shared features for event detection using few exemplars

Ma, Z., Yang, Y., Sebe, N. and Hauptmann, A. G. (2014) Knowledge adaptation with partially shared features for event detection using few exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 9: 1789-1802. doi:10.1109/TPAMI.2014.2306419

Author Ma, Z.
Yang, Y.
Sebe, N.
Hauptmann, A. G.
Title Knowledge adaptation with partially shared features for event detection using few exemplars
Journal name IEEE Transactions on Pattern Analysis and Machine Intelligence   Check publisher's open access policy
ISSN 0162-8828
Publication date 2014-09
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TPAMI.2014.2306419
Open Access Status
Volume 36
Issue 9
Start page 1789
End page 1802
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2015
Language eng
Abstract Multimedia event detection (MED) is an emerging area of research. Previous work mainly focuses on simple event detection in sports and news videos, or abnormality detection in surveillance videos. In contrast, we focus on detecting more complicated and generic events that gain more users' interest, and we explore an effective solution for MED. Moreover, our solution only uses few positive examples since precisely labeled multimedia content is scarce in the real world. As the information from these few positive examples is limited, we propose using knowledge adaptation to facilitate event detection. Different from the state of the art, our algorithm is able to adapt knowledge from another source for MED even if the features of the source and the target are partially different, but overlapping. Avoiding the requirement that the two domains are consistent in feature types is desirable as data collection platforms change or augment their capabilities and we should be able to respond to this with little or no effort. We perform extensive experiments on real-world multimedia archives consisting of several challenging events. The results show that our approach outperforms several other state-of-the-art detection algorithms.
Keyword Multimedia event detection (MED)
Knowledge adaptation
Heterogenous features
Heterogeneous features based structural adaptive regression (HF-SAR)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 22 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 19 Aug 2014, 02:54:01 EST by System User on behalf of School of Information Technol and Elec Engineering