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Towards training-free refinement for semantic indexing of visual media

Wang, Peng, Sun, Lifeng, Yang, Shiqiang and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2016) Towards training-free refinement for semantic indexing of visual media. Proceedings of Multimedia Modelling, Miami, Florida, 4-6 January 2016, LNCS 9 . pp. 251-263.

Abstract
Indexing of visual media based on content analysis has now moved beyond using individual concept detectors and there is now a fo- cus on combining concepts or post-processing the outputs of individual concept detection. Due to the limitations and availability of training cor- pora which are usually sparsely and imprecisely labeled, training-based refinement methods for semantic indexing of visual media suffer in cor- rectly capturing relationships between concepts, including co-occurrence and ontological relationships. In contrast to training-dependent methods which dominate this field, this paper presents a training-free refinement (TFR) algorithm for enhancing semantic indexing of visual media based purely on concept detection results, making the refinement of initial con- cept detections based on semantic enhancement, practical and flexible. This is achieved using global and temporal neighbourhood information inferred from the original concept detections in terms of weighted non- negative matrix factorization and neighbourhood-based graph propaga- tion, respectively. Any available ontological concept relationships can also be integrated into this model as an additional source of external a priori knowledge. Experiments on two datasets demonstrate the efficacy of the proposed TFR solution.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Semantic indexing; Refinement; Concept detection enhancement; Context fusion; Factorization; Propagation
Subjects:Computer Science > Lifelog
Computer Science > Multimedia systems
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
ID Code:21010
Deposited On:26 Jan 2016 14:55 by Alan Smeaton . Last Modified 31 Oct 2018 11:34
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