Robustness comparison of clustering - based vs. non-clustering multi-label classifications for image and video annotations
Data(s) |
01/01/2015
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30081752/sajjanhar-robustnesscomparison-2015.pdf http://dro.deakin.edu.au/eserv/DU:30081752/sajjanhar-robustnesscomparison-evid-2015.pdf http://www.dx.doi.org/10.1109/CISP.2015.7407966 |
Direitos |
2015, IEEE |
Palavras-Chave | #multi-concept #image and video annotation #clustering based #multi-label classification #robustness comparison |
Tipo |
Conference Paper |