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What are the limits to time series based recognition of semantic concepts?

Wang, Peng, Sun, Lifeng, Yang, Shiqiang and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2016) What are the limits to time series based recognition of semantic concepts? In Proceedings of Multimedia Modelling, Miama, Florida, US, 4 to 6 January 2016., 9517 .

Abstract
Most concept recognition in visual multimedia is based on relatively simple concepts, things which are present in the image or video. These usually correspond to objects which can be identified in images or individual frames. Yet there is also a need to recognise semantic con- cepts which have a temporal aspect corresponding to activities or com- plex events. These require some form of time series for recognition and also require some individual concepts to be detected so as to utilise their time-varying features, such as co-occurrence and re-occurrence patterns. While results are reported in the literature of using concept detections which are relatively specific and static, there are research questions which remain unanswered. What concept detection accuracies are satisfactory for time series recognition? Can recognition methods perform equally well across various concept detection performances? What affecting factors need to be taken into account when building concept-based high-level event/activity recognitions? In this paper, we conducted experiments to investigate these questions. Results show that though improving concept detection accuracies can enhance the recognition of time series based concepts, they do not need to be very accurate in order to characterize the dynamic evolution of time series if appropriate methods are used. Experimental results also point out the importance of concept selec- tion for time series recognition, which is usually ignored in the current literature.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Concept detection; Time series; Activity recognition; Attribute dynamics; Event classification
Subjects:Computer Science > Information technology
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Springer LNCS
Copyright Information:© 2016 Springer The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:National Natural Science Foundation of China, Science Foundation Ireland (SFI/12/RC/2289)
ID Code:21011
Deposited On:26 Jan 2016 14:54 by Alan Smeaton . Last Modified 31 Oct 2018 11:34
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