Name
Abstract | ||
|---|---|---|
•A novel unsupervised learning method that exploits Cartesian product of rankings.•An automatic approach proposed for neighborhood size estimation.•Approximate execution allows its use for queries which are not part of the dataset.•Experimental evaluation in seven different multimedia datasets (images and videos).•Results show effectiveness and efficiency gains compared with the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patrec.2017.10.013 | Pattern Recognition Letters |
Keywords | Field | DocType |
Content-based image retrieval,Unsupervised learning,Cartesian product,Effectiveness,Efficiency | Similarity learning,Data mining,Cartesian product,Computer science,Symmetric multiprocessor system,Multimedia information retrieval,Unsupervised learning,Artificial intelligence,Computer vision,Pattern recognition,Ranking,Content-based image retrieval,Machine learning | Journal |
Volume | ISSN | Citations |
114 | 0167-8655 | 0 |
PageRank | References | Authors |
0.34 | 45 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Pascotti Valem | 1 | 7 | 5.80 |
| Daniel Carlos Guimarães Pedronette | 2 | 304 | 25.47 |
| Jurandy Almeida | 3 | 431 | 35.15 |