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首页> 外文期刊>International Journal of Business Intelligence and Data Mining >Using diverse set of features to design a content-based video retrieval system optimised by gravitational search algorithm
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Using diverse set of features to design a content-based video retrieval system optimised by gravitational search algorithm

机译：使用不同的功能集设计基于内容的视频检索系统，通过引力搜索算法优化

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This paper explains about the content-based video retrieval approach (CBVR) using four varieties of features and 12 distance measurements, which is optimised by gravitational search algorithm (GSA). Initially, CBVR technique extracts five kinds of features such as colour, texture, shape, image and audio features that belong to each frame. Consequently, it emerges particular distance measurements for every sort of features to compute the similarity between query frame and remaining in the database frame. In this paper, we have used GSA to find the nearly optimal combination between the features and their respective similarity measurements. At last, from the video database, the query-based videos are recovered. For experimentation, here we used two types of databases such as sports video and UCF sports action datasets. The experimental results demonstrate that the proposed CBVR method shows better performance when contrasted with other existing methods.
机译：本文利用四种特征和12个距离测量来介绍基于内容的视频检索方法（CBVR），这是由引力搜索算法（GSA）进行优化的。最初，CBVR技术提取五种功能，例如属于每个帧的颜色，纹理，形状，图像和音频功能。因此，它出现了针对各种特征的特定距离测量来计算查询帧之间的相似性并留在数据库帧中。在本文中，我们使用GSA在特征和各自的相似度测量之间找到几乎最佳的组合。最后，从视频数据库中，恢复了基于查询的视频。对于实验，在这里，我们使用了两种类型的数据库，例如运动视频和UCF体育动作数据集。实验结果表明，当与其他现有方法形成鲜明对比时，所提出的CBVR方法表现出更好的性能。

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