Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3473–3491 | Cite as

Multimedia automatic annotation by mining label set correlation

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Abstract

Organizing and retrieving multimedia data heavily rely on the relevant textual descriptions. Multimedia automatic annotation, which assigns text labels to multimedia samples, has been widely studied. Among others, search-based annotation methods are well suited for annotation tasks on large-scale datasets and are studied in depth because of their simplicity and scalability. However, classical search based annotation methods address this problem by treating each label independently, which ignores the correlation between different labels in the assigned label set. This paper aims to integrate the relevant information of the label set with respect to the multimedia content and the inner correlated information of the label set into a joint learning framework. We evaluate the performance of the proposed method on MIRFLICKR-25000 and NUS-WIDE datasets. Experimental results show that the proposed annotation method achieves excellent performance.

Keywords

Multimedia annotation Automatic annotation Label set correlation mining 

Notes

Acknowledgements

Special thanks should go to the collaborators in the Lab for Media Search of National University of Singapore, for their instructive advice and useful suggestions on this work. This work is supported by the Natural Science Foundation of China (No.61502094,61402099) and Natural Science Foundation of Heilongjiang Province of China(No.F2016002,F2015020).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyNortheast Petroleum UniversityDaQingChina
  2. 2.State Key Laboratory of Virtual Reality Technology and SystemsBeiHang UniversityBeijingChina

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