| Abstrakt: |
In today's fast-moving digital era, video technology plays an important role. An effective video summarising approach is urgently needed to handle a lot of video data due to the ever-growing number of video content. In this paper, the authors have proposed, A hybrid summarization methodology for video summary evaluation using multimedia features (Text, Images, and Audio) that assess how well a video summary can keep the ranking of vital video frames, semantic data, and audio present in the original video. Video summary can be evaluated by ranking text, audio, and semantics of video frames, giving more accurate summarisation results. The proposed methodology works in three phases: The first part takes the text in the video, the second phase takes the audio to the file, and the last phase focuses on the video frames rather than images in the video. TVSum dataset has been used for the experimentation. F1 has been used as the evaluation metric for checking the efficacy and efficiency of the proposed methodology. The result shows that the proposed hybrid model achieves the highest F1 score of 69.9% and saves 75-80% of user time in watching video summaries instead of the whole video. [ABSTRACT FROM AUTHOR] |
