Static video summarization with multi-objective constrained optimization

3区计算机科学 Q1 Computer Science

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-01 DOI:10.1007/s12652-024-04777-z

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引用次数: 0

Abstract

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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利用多目标约束优化进行静态视频总结

摘要视频摘要是一个新兴的研究领域。其中，静态视频摘要在视频库的抽象和索引中发挥着重要作用。它能提取视频中的重要事件，从而涵盖视频的全部内容。包含这些重要事件的帧称为关键帧，最终用于视频索引。它还有助于提供视频内容的抽象视图，使互联网用户在完整观看视频之前就能了解视频中出现的事件。拟议的研究工作侧重于通过提取各种视觉特征（即颜色、纹理和形状特征）来实现高效的静态视频摘要。使用基于密度的带噪声应用空间聚类（DBSCAN）算法对这些特征进行聚合和聚类。为了通过聚类产生良好的视频摘要，DBSCAN 算法的参数采用了一种名为人工藻类算法（AAA）的基于群体的元启发式优化方法进行优化。在两个公共数据集（即 VSUMM 和 OVP 数据集）上的实验结果表明，与现有方法相比，所提出的多目标约束优化静态视频摘要算法（SVS_MCO）取得了更好的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

9.60

自引率

0.00%

发文量

854

期刊介绍： The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators