An automated approach to retrieve lecture videos using context based semantic features and 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 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.