Abstract
In recent years, extensive research efforts have been dedicated to automatic news content analysis. In this paper, we propose a novel algorithm for anchorperson detection in news video sequences. In this method, the raw news videos are firstly split into shots by a four-threshold method, and the key frames are extracted from each shot. After that, the anchorperson detection is conducted from these key frames by using a clustering-based method based on a statistical distance of Pearson's correlation coefficient. To evaluate the effectiveness of the proposed method, we have conducted experiments on 10 news sequences. In these experiments, the proposed scheme achieves a recall of 0.96 and a precision of 0.97 for anchorperson detection.
| Original language | English |
|---|---|
| Pages (from-to) | 86-99 |
| Number of pages | 14 |
| Journal | Neurocomputing |
| Volume | 123 |
| DOIs | |
| Publication status | Published - 10 Jan 2014 |
Keywords
- Anchorperson detection
- Clustering
- News video analysis
- Shot detection