Video digitization is one of the emerging technologies holding significant importance over years in applications like video recording and video compression. There are different techniques available in the literature for the effective retrieval of videos. This research work presents a video retrieval scheme based on a deep learning strategy. Initially, the video archive is subjected to the keyframe extraction, for extracting useful keyframes from the video. The features extracted from the keyframes are stored in the feature database. The features are clustered using the Fuzzy C Means (FCM) algorithm. These clustered features have been provided to the deep learner for finding the optimal centroid for the incoming user query. For experimentation, the research has considered videos from different categories, and both the text query and the video query have been used for the retrieval. The experimental results demonstrate the efficiency of the proposed deep learning strategy in video retrieval and its achievement of improved values of 0.9620, 0.9682, and 0.9652 respectively for recall, precision, and F-measure.

视频数字化是新兴技术之一，多年来在诸如视频记录和视频压缩的应用中具有极其重要的意义。文献中提供了多种有效检索视频的技术。这项研究工作提出了一种基于深度学习策略的视频检索方案。最初，对视频档案进行关键帧提取，以从视频中提取有用的关键帧。从关键帧提取的特征存储在特征数据库中。使用模糊C均值（FCM）算法对特征进行聚类。这些聚类的功能已提供给深度学习者，以为传入的用户查询找到最佳质心。为了进行实验，研究考虑了不同类别的视频，文本查询和视频查询都已用于检索。实验结果证明了所提出的深度学习策略在视频检索中的效率，以及针对召回率，精度和F量度的改进值，分别达到0.9620、0.9682和0.9652。