| Autor: |
Archana, N., Malmurugan, N. |
| Zdroj: |
Journal of Ambient Intelligence & Humanized Computing; May2021, Vol. 12 Issue 5, p5381-5395, 15p |
| Abstrakt: |
Video summarization is an inevitable process in this developed communication world. The improvements in digital communication and filmless video recording technologies triggered the tremendous growth of storing and sharing variety of videos. Video summarization is used to optimize the searching and organizing process of different types of videos. Precision, Recall, F-Score and Processing time are the primary evaluation metrics of a video summarization procedure. A frequency domain multi-edge detection process and Multi-Edge optimized Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) are proposed and integrated in this work. The frequency domain Multi-Edge detection is introduced to improve the precision, recall and F-Score whereas, Multi-Edge Optimized LSTM is used to reduce the processing time of the summarization process. Discrete Wavelet Transformation based multi-edge detection algorithm is introduced and integrated with the optimized LSTM to achieve the betterment of the summarization process. The proposed method named as Multi-Edge optimized LSTM RNN for Video Summarization (MOLRVS) is indented to perform the video summarization process in real time video streaming environments to reduce a significant amount of manual interventions. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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