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Video retrieval; keyframes; clustering; deep learning

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 literaturefor 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, theresearch 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 strategyin video retrieval and its achievement of improved values of 0.9620, 0.9682, and 0.9652 respectively for recall, precision, and F-measure

N POORNIMA^{1 2} B SALEENA¹

1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
2. School of Computing, SRM Institute of Science and Technology, Chennai, India

Published

8 October 2020

Manuscript received

17 February 2020

Manuscript revised

9 July 2020

Accepted

17 August 2020