.png){kind=icon}
Return
Multi video summarization using query based deep optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-023-01852-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The popularity of online video-sharing platforms has fuelled demand for systems that can quickly browse, extract, and summarise video information. Nowadays, numerous automatic multi-video summarization (MVS) techniques have come into existence. The existing MVS approach, on the other hand, produces summarised video with a lot of unimportant and duplicate frames. It also arranges frames in a meaningless manner. To solve these problems in MVS, Query-based Deep African Vulture Learning (QDAVOL) is proposed in this paper. It uses tag information and web images searched by the query as important information to identify the query intent. An event-based object detection and grouping (EODG) technique is used to assign keyframes to groups of specific events relevant with the query. In addition, we introduce the African vulture optimization algorithm (AVOA) for the efficient key frame selection. Moreover, we have also developed a similarity-based frame closeness (SFC) technique to provide more comprehensible summary. Experimental results demonstrate that the proposed framework outperforms existing approaches in terms of precision (0.765), recall (0.845), and average F-score (0.774) on MVS1K dataset.
Keywords:
Multi-video summarization
African vulture optimization algorithm
Key frame selection
Object detection
Query based summarization
Journal
IF:
2.7
Papers:
3.2K
Citations:
5.6K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSaveShareSave
View more