Name
Abstract | ||
|---|---|---|
In this paper, a new learned basis set algorithm (3D-LBT) based on 3D-DCT (Discrete Cosine Transform) is proposed for video fingerprinting and matching, in which for different video categories an Adaboost-based machine learning method is applied to each category of videos for selecting suitable sets of 3D-DCT coefficients to generate fingerprints, and a weighted distance of fingerprints is also defined for fingerprint matching. Our experimental results have illustrated that the proposed algorithm outperforms the conventional 3D-DCT algorithm and the 3D-RBT (Randomized Basis seT) algorithm in terms of robustness and uniqueness. Moreover, the proposed algorithm has better security performance for copyright applications. |
Year | Venue | Keywords |
|---|---|---|
2013 | ISPA | video fingerprint,copyright protection,machine learning,3D-DCT,weighted distance,adaboost |
Field | DocType | ISSN |
Uniqueness,Weighted distance,AdaBoost,Pattern recognition,Computer science,Discrete cosine transform,Algorithm,Fingerprint,Robustness (computer science),Artificial intelligence,Discrete cosine transforms | Conference | 1845-5921 |
ISBN | Citations | PageRank |
978-953-184-194-8; 978-953-184-187-0 | 2 | 0.37 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diao Mengge | 1 | 4 | 0.74 |
| Zhu Yuesheng | 2 | 112 | 39.21 |
| Sun Ziqiang | 3 | 15 | 3.97 |
| Liu Xiyao | 4 | 5 | 4.17 |
| Zhang Limin | 5 | 2 | 0.37 |