Name
Abstract | ||
|---|---|---|
Near-duplicate image detection plays an important role in several real applications. Such task is usually achieved by applying a clustering algorithm followed by refinement steps, which is a computationally expensive process. In this paper we introduce a framework based on a novel similarity join operator, which is able both to replace and speed up the clustering step, whereas also releasing the need of further refinement processes. It is based on absolute and relative similarity ratios, ensuring that top ranked image pairs are in the final result. Experiments performed on real datasets shows that our proposal is up to three orders of magnitude faster than the best techniques in the literature, always returning a high-quality result set. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISM.2015.114 | 2015 IEEE International Symposium on Multimedia (ISM) |
Keywords | Field | DocType |
near-duplicate,similarity join,wide-join | Fuzzy clustering,Joins,Result set,Ranking,Pattern recognition,Correlation clustering,Computer science,Artificial intelligence,Cluster analysis,Self-similarity,Speedup | Conference |
Citations | PageRank | References |
1 | 0.39 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luiz Olmes Carvalho | 1 | 5 | 3.56 |
| Lucio F. D. Santos | 2 | 25 | 6.76 |
| Willian D. Oliveira | 3 | 21 | 5.98 |
| Agma J. M. Traina | 4 | 1024 | 153.61 |
| Caetano Traina Jr. | 5 | 1052 | 137.26 |