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Semantically smoothed refinement for everyday concept indexing

Wang, Peng, Sun, Lifeng, Yang, Shiqiang and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2016) Semantically smoothed refinement for everyday concept indexing. In: 17th Pacific Rim Confereence on Multimedia (PCM), 15-16 September 2016, Xi'an, China.

Abstract
Instead of occurring independently, semantic concepts pairs tend to co-occur within a single image and it is intuitive that concept detection accuracy for visual concepts can be enhanced if concept correlation can be leveraged in some way. In everyday concept detection for visual lifelogging using wearable cameras to automatically record every- day activities, the captured images usually have a diversity of concepts which challenges the performance of concept detection. In this paper a semantically smoothed refinement algorithm is proposed using concept correlations which exploit topic-related concept relationships, modeled externally in a user experiment rather than extracted from training data. Results for initial concept detection are factorized based on semantic smoothness and adjusted in compliance with the extracted concept correlations. Refinement performance is demonstrated in experiments to show the effectiveness of our algorithm and the extracted correlations.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Seminar
Refereed:Yes
Uncontrolled Keywords:Semantic indexing; concept refinement; detection refinement; semantic smoothness; lifelogging.
Subjects:Computer Science > Lifelog
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
Published in: Advances in Multimedia Information Processing - PCM 2016. Lecture Notes in Computer Science (LNCS) 9916. Springer.
Publisher:Springer
Official URL:http://dx.doi.org/10.1007/978-3-319-48890-5_31
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:Science Foundation Ireland SFI/12/RC/2289., National Natural Science Foundation of China Grant No. 2011CB302206, National Natural Science Foundation of China Grant No. 61272231, 61472204, 61502264
ID Code:21508
Deposited On:08 Dec 2016 16:08 by Alan Smeaton . Last Modified 31 Oct 2018 11:36
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