Abstract
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.
Highlights
The rapid increase in video data invokes the necessity for efficient indexing and retrieval systems
It seems crucial to have automatic video content analysis systems capable of representing, modeling, indexing, retrieving, browsing, or searching information stored in large multimedia databases
These techniques are grouped into a single concept of Content-Based Video Retrieval systems (CBVR) [1, 2]
Summary
The rapid increase in video data invokes the necessity for efficient indexing and retrieval systems. The first category focuses on annotation-based approaches, which employ textual information, attributes, or keyword annotations to represent the video content. With the massive amount of video data being generated every day, using textual descriptions is no longer practical. Because they take an enormous amount of time. It seems crucial to have automatic video content analysis systems capable of representing, modeling, indexing, retrieving, browsing, or searching information stored in large multimedia databases. These techniques are grouped into a single concept of Content-Based Video Retrieval systems (CBVR) [1, 2]
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