支持深度学习的讲座音频视频摘要优化

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI:10.1016/j.jvcir.2024.104309

Preet Chandan Kaur , Dr. Leena Ragha

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引用次数: 0

摘要

视频摘要通过将冗长的视频内容压缩成压缩表示法，在多种应用中发挥着重要作用。本研究的目的是提出一种用于讲座音频视频摘要的微调深度模型。首先，从数据集中获取输入的讲座视听视频。然后，使用 YCbCr 空间颜色模型进行视频镜头分割（幻灯片分割）。使用基于蜜獾的白头鹰算法（HBBEA）对每个视频镜头中的音频和视频进行分割。HBBEA 结合了秃鹰搜索（BES）和蜜獾算法（HBA）。通过 HBBEA 执行 DRN 训练，以选择最佳 DRN 权重。相关视频帧与音频合并。所提出的基于 HBBEA 算法的 DRN 性能更优，F1 分数为 91.9 %，负预测值 (NPV) 为 89.6 %，正预测值 (PPV) 为 90.7 %，准确率为 91.8 %，精确度为 91 %，召回率为 92.8 %。

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Optimized deep learning enabled lecture audio video summarization

Video summarization plays an important role in multiple applications by compressing lengthy video content into compressed representation. The purpose is to present a fine-tuned deep model for lecture audio video summarization. Initially, the input lecture audio-visual video is taken from the dataset. Then, the video shot segmentation (slide segmentation) is done using the YCbCr space colour model. From each video shot, the audio and video within the video shot are segmented using the Honey Badger-based Bald Eagle Algorithm (HBBEA). The HBBEA is obtained by combining the Bald Eagle Search (BES) and Honey Badger Algorithm (HBA). The DRN training is executed by HBBEA to select the finest DRN weights. The relevant video frames are merged with the audio. The proposed HBBEA-based DRN outperformed with a better F1-Score of 91.9 %, Negative predictive value (NPV) of 89.6 %, Positive predictive value (PPV) of 90.7 %, Accuracy of 91.8 %, precision of 91 %, and recall of 92.8 %.

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来源期刊

Journal of Visual Communication and Image Representation 工程技术-计算机：软件工程

CiteScore

5.40

自引率

11.50%

发文量

188

审稿时长

9.9 months

期刊介绍： The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.