Static video summarization with multi-objective constrained optimization

Video summarization is an emerging research field. In particular, static video summarization plays a major role in abstraction and indexing of video repositories. It extracts the vital events in a video such that it covers the entire content of the video. Frames having those important events are cal...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing Vol. 15; no. 4; pp. 2621 - 2639
Main Authors: Dhanushree, M., Priya, R., Aruna, P., Bhavani, R.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Clustering
Computational Intelligence
Datasets
Engineering
Indexing
Multiple objective analysis
Optimization
Original Research
Robotics and Automation
Social networks
Surveillance
User Interfaces and Human Computer Interaction
Video data
DBSCAN algorithm
Keyframes
Multi-objective optimization
Static video summarization
Artificial Algae Algorithm
ISSN:1868-5137, 1868-5145
Online Access:Get full text
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Summary:Video summarization is an emerging research field. In particular, static video summarization plays a major role in abstraction and indexing of video repositories. It extracts the vital events in a video such that it covers the entire content of the video. Frames having those important events are called keyframes which are eventually used in video indexing. It also helps in giving an abstract view of the video content such that the internet users are aware of the events present in the video before watching it completely. The proposed research work is focused on efficient static video summarization by extracting various visual features namely color, texture and shape features. These features are aggregated and clustered using a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to produce good video summary by clustering, the parameters of DBSCAN algorithm are optimized by using a meta heuristic population based optimization called Artificial Algae Algorithm (AAA). The experimental results on two public datasets namely VSUMM and OVP dataset show that the proposed Static Video Summarization with Multi-objective Constrained Optimization (SVS_MCO) achieves better results when compared to existing methods.
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-024-04777-z