自动提取多模态特征与视频质量的相关性研究
Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality
摘要：
多媒体内容（例如视频）的排名和推荐通常相对于与用户查询的相关性来实现。然而，对于讲座视频和MOOC（大规模开放式在线课程），不仅需要检索相关视频，而且特别需要找到促进学习的高质量讲座视频，例如，独立于视频或演讲者的受欢迎程度。因此，关于讲座视频质量的元数据是学习上下文的关键特征，例如，讲座视频推荐在搜索作为学习场景。在本文中，我们调查是否自动提取的功能相关的视频质量方面。一组来自大众开放式在线课程（MOOC）的学术视频进行了音频，语言和视觉特征的分析。此外，提出了一组跨模态的功能，这是通过结合成绩单，音频，视频和幻灯片内容。进行用户研究，调查自动收集的功能和人类的评价质量方面的讲座视频之间的相关性。最后，我们的功能上的知识获得的参与者的影响进行了讨论。
英文摘要：
Ranking and recommendation of multimedia content such as videos is usually realized with respect to the relevance to a user query. However, for lecture videos and MOOCs (Massive Open Online Courses) it is not only required to retrieve relevant videos, but particularly to find lecture videos of high quality that facilitate learning, for instance, independent of the video's or speaker's popularity. Thus, metadata about a lecture video's quality are crucial features for learning contexts, e.g., lecture video recommendation in search as learning scenarios. In this paper, we investigate whether automatically extracted features are correlated to quality aspects of a video. A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed regarding audio, linguistic, and visual features. Furthermore, a set of cross-modal features is proposed which are derived by combining transcripts, audio, video, and slide content. A user study is conducted to investigate the correlations between the automatically collected features and human ratings of quality aspects of a lecture video. Finally, the impact of our features on the knowledge gain of the participants is discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
