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Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Gupta,S, Phung,D and Venkatesh,S 2014, Modelling multilevel data in multimedia : A hierarchical factor analysis approach, Multimedia Tools and Applications, pp. 1-23, doi: 10.1007/s11042-014-2394-3.

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Title Modelling multilevel data in multimedia : A hierarchical factor analysis approach
Author(s) Gupta,SORCID iD for Gupta,S orcid.org/0000-0002-3308-1930
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Venkatesh,SORCID iD for Venkatesh,S orcid.org/0000-0001-8675-6631
Journal name Multimedia Tools and Applications
Start page 1
End page 23
Total pages 23
Publisher Springer
Place of publication New York, New York
Publication date 2014-12-12
ISSN 1380-7501
1573-7721
Keyword(s) Bayesian nonparametrics
Beta process
Dirichlet process
Multilevel data
Multimedia
Semantic gap
Summary Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.
Language eng
DOI 10.1007/s11042-014-2394-3
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071997

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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