Despite enormous research efforts devoted by the research community to effectively and precisely perform video matching and retrieval among heterogeneous videos from large-scale video repositories still remains a complex and most challenging task. In order to address this complex challenge, a content based video retrieval technique is required, which can exploit the visual content of the videos for effective retrieval from the videos repositories. In our proposed method, we introduce a computer assisted video retrieval technique which can retrieve the visually similar videos stored in the repositories. To accomplish this task, video summarization based on motion vector is employed to select keyframes based on similar segments. To estimate the video content, salient foreground extraction is executed, and matching based on the spatial pyramid is employed for matching the keyframe features of query video with videos in the repositories. The contribution of the former process has two major sections for superior saliency map generation. Firstly, it heuristically integrates the regional property, contrast, and foreground descriptors together. Secondly, it introduces a new feature vector to characterize the foreground as an object descriptor, while the latter process is the extension of orderless bag-of-features representation, which has significant performance with respect to scene categorization. The video retrieval performance is compared with standard state-of-the-art techniques using real-time datasets. Experimental and usability studies provide satisfactory results for video retrieval based on evaluation metrics such as video sampling error, fidelity, precision, and recall.

尽管研究界做出了巨大的研究努力，以有效且精确地在来自大型视频存储库的异构视频之间进行视频匹配和检索仍然是一项复杂且最具挑战性的任务。为了解决这个复杂的挑战，需要一种基于内容的视频检索技术，该技术可以利用视频的视觉内容来从视频存储库中进行有效检索。在我们提出的方法中，我们引入了一种计算机辅助视频检索技术，该技术可以检索存储在存储库中的视觉相似的视频。为了完成此任务，基于运动矢量的视频摘要用于基于相似段选择关键帧。为了估算视频内容，需要执行显着前景提取，基于空间金字塔的匹配用于将查询视频的关键帧特征与存储库中的视频进行匹配。前一过程的贡献有两个主要部分，用于生成卓越的显着性图。首先，它启发式地将区域属性，对比度和前景描述符集成在一起。其次，它引入了一个新的特征向量来将前景表征为一个对象描述符，而后一个过程是无序特征包表示的扩展，它在场景分类方面具有显着的性能。使用实时数据集，将视频检索性能与标准最新技术进行了比较。实验和可用性研究根据评估指标（例如视频采样误差，