Name
Abstract | ||
|---|---|---|
Multiple-labeling classification approaches attempt to handle applications that associate more than one label to a given sample. Since we have an increasing number of systems that are guided by such assumption, in this paper we have presented a multiple-labeling approach for the Optimum-Path Forest (OPF) classifier based on the problem transformation method. In order to validate our proposal, a multi-labeled video classification dataset has been used to compare OPF against three other classifiers and another variant of the OPF classifier based on a k-neighborhood. The results have shown the validity of the OPF-based classifiers for multi-labeling classification problems. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/SIBGRAPI.2013.53 | SIBGRAPI |
Keywords | Field | DocType |
pattern classification,video signal processing,OPF classifier,k-neighborhood,multilabeled video classification,multiple labeling-based optimum-path forest,multiple-labeling classification,video content classification,Image motion analysis,Optimum-Path Forest,Video signal classification,multi-label learning | Pattern recognition,Computer science,Visualization,Problem transformation,Multi label learning,Artificial intelligence,Classifier (linguistics),Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luís A. M. Pereira | 1 | 129 | 8.87 |
| J. Paulo Papa | 2 | 1 | 0.35 |
| Jurandy Almeida | 3 | 431 | 35.15 |
| Ricardo da S. Torres | 4 | 805 | 45.77 |
| Willian Paraguassu Amorim | 5 | 24 | 4.52 |