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Robustness comparison of clustering - based vs. non-clustering multi-label classifications for image and video annotations

Nasierding, Gulisong, Li, Yong and Sajjanhar, Atul 2015, Robustness comparison of clustering - based vs. non-clustering multi-label classifications for image and video annotations, in CISP 2015 : Proceedings of the 8th International Congress on Image and Signal Processing, IEEE, Piscataway, N.J., pp. 691-696, doi: 10.1109/CISP.2015.7407966.

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Title Robustness comparison of clustering - based vs. non-clustering multi-label classifications for image and video annotations
Author(s) Nasierding, Gulisong
Li, Yong
Sajjanhar, AtulORCID iD for Sajjanhar, Atul orcid.org/0000-0002-0445-0573
Conference name Image and Signal Processing. International Congress (8th : 2015 : Shenyang, China)
Conference location Shenyang, China
Conference dates 14-16 Oct. 2015
Title of proceedings CISP 2015 : Proceedings of the 8th International Congress on Image and Signal Processing
Publication date 2015
Start page 691
End page 696
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) multi-concept
image and video annotation
clustering based
multi-label classification
robustness comparison
Summary This paper reports robustness comparison of clustering-based multi-label classification methods versus nonclustering counterparts for multi-concept associated image and video annotations. In the experimental setting of this paper, we adopted six popular multi-label classification Algorithms, two different base classifiers for problem transformation based multilabel classifications, and three different clustering algorithms for pre-clustering of the training data. We conducted experimental evaluation on two multi-label benchmark datasets: scene image data and mediamill video data. We also employed two multi-label classification evaluation metrics, namely, micro F1-measure and Hamming-loss to present the predictive performance of the classifications. The results reveal that different base classifiers and clustering methods contribute differently to the performance of the multi-label classifications. Overall, the pre-clustering methods improve the effectiveness of multi-label classifications in certain experimental settings. This provides vital information to users when deciding which multi-label classification method to choose for multiple-concept associated image and video annotations.
ISBN 9781467390989
Language eng
DOI 10.1109/CISP.2015.7407966
Field of Research 080106 Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081752

Document type: Conference Paper
Collection: School of Information Technology
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