With the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video Retrieval (CBVR) is nowadays an active area of research. In this article, a CBVR system providing similar videos from a large multimedia dataset based on query video has been proposed. This approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key frames for rapid browsing and efficient video indexing. The proposed method has been implemented on both single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments were performed using various benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to previous studies.

随着视频数据量的快速增长，高效的视频索引和检索方法已成为多媒体管理中最关键的挑战之一。为此，基于内容的视频检索 (CBVR) 是当今一个活跃的研究领域。在本文中，提出了一种基于查询视频从大型多媒体数据集中提供相似视频的 CBVR 系统。这种方法使用基于矢量运动的签名来描述视觉内容，并使用机器学习技术提取关键帧以实现快速浏览和高效视频索引。所提出的方法已在单机和实时分布式集群上实现，以评估实时性能方面，尤其是在视频数量和大小较大时。