Name
Abstract | ||
|---|---|---|
This paper presents a method for content change detection in multidimensional video signals. Video frames are represented as tensors of order consistent with signal dimensions. The method operates on unprocessed signals and no special feature extraction is assumed. The dynamic tensor analysis method is used to build a tensor model from the stream. Each new datum in the stream is then compared to the model with the proposed concept drift detector. If it fits, then a model is updated. Otherwise, a model is rebuilt, starting from that datum, and the signal shot is recorded. The proposed fast tensor decomposition algorithm allows efficient operation compared to the standard tensor decomposition method. Experimental results show many useful properties of the method, as well as its potential further extensions and applications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00354-017-0024-0 | New Generation Comput. |
Keywords | Field | DocType |
Video shot detection, Anomaly detection, Tensor decomposition, Tensor frames, Dynamic tensor analysis | Anomaly detection,Computer vision,Geodetic datum,Change detection,Tensor,Computer science,Feature extraction,Concept drift,Theoretical computer science,Artificial intelligence,Multilinear subspace learning,Detector | Journal |
Volume | Issue | ISSN |
35 | 4 | 0288-3635 |
Citations | PageRank | References |
4 | 0.43 | 31 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boguslaw Cyganek | 1 | 145 | 24.53 |
| Michał Woźniak | 2 | 213 | 24.64 |