.png){kind=icon}
返回
Fluctuation-Based Fade Detection for Local Scene Changes
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3125731.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, fade detection algorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes and scenes with captions or flashing light sources. This paper proposes a new fade detection algorithm that uses similarity tendencies of luminance transitions to overcome such obstacles. To prevent detection accuracy degradation by letterboxing and captions, video frames are simplified. Then, fade candidates are detected by transition boundary detection using the angular and curvature characteristics of the luminance vectors. Finally, luminance flipping detection improves the detection accuracy by extracting the luminance retrograde phenomenon that occurs with flashing or light source movements. Through objective evaluation using F-1 score, the detection accuracy of the proposed algorithm was 0.884, which is an increase of 0.187 (21.2% improvement) compared with the average F-1 score of existing high-performance methods.
Keyword:
Image edge detection
Histograms
Feature extraction
Light sources
Image color analysis
Detection algorithms
Standards
Algorithm design and analysis
change detection algorithms
machine learning algorithms
video signal processing
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容