Video Abstracting at a Semantical Level

One the most common form of a video abstract is the movie trailer. Contemporary movie trailers share a common structure across genres which allows for an automatic generation and also reflects the corresponding moviea s composition. In this thesis a system for the automatic generation of trailers is presented. In addition to action trailers, the system is able to deal with further genres such as Horror and comedy trailers, which were first manually analyzed in order to identify their basic structures. To simplify the modeling of trailers and the abstract generation itself a new video abstracting application was developed. This application is capable of performing all steps of the abstract generation automatically and allows for previews and manual optimizations. Based on this system, new abstracting models for horror and comedy trailers were created and the corresponding trailers have been automatically generated using the new abstracting models. In an evaluation the automatic trailers were compared to the original Trailers and showed a similar structure. However, the automatically generated trailers still do not exhibit the full perfection of the Hollywood originals as they lack intentional storylines across shots

PodReels: Human-AI Co-Creation of Video Podcast Teasers

Video podcast teasers are short videos that can be shared on social media platforms to capture interest in the full episodes of a video podcast. These teasers enable long-form podcasters to reach new audiences and gain new followers. However, creating a compelling teaser from an hour-long episode is challenging. Selecting interesting clips requires significant mental effort; editing the chosen clips into a cohesive, well-produced teaser is time-consuming. To support the creation of video podcast teasers, we first investigate what makes a good teaser. We combine insights from both audience comments and creator interviews to determine a set of essential ingredients. We also identify a common workflow shared by creators during the process. Based on these findings, we introduce a human-AI co-creative tool called PodReels to assist video podcasters in creating teasers. Our user study shows that PodReels significantly reduces creators' mental demand and improves their efficiency in producing video podcast teasers

Digital tools in media studies: analysis and research. An overview

Digital tools are increasingly used in media studies, opening up new perspectives for research and analysis, while creating new problems at the same time. In this volume, international media scholars and computer scientists present their projects, varying from powerful film-historical databases to automatic video analysis software, discussing their application of digital tools and reporting on their results. This book is the first publication of its kind and a helpful guide to both media scholars and computer scientists who intend to use digital tools in their research, providing information on applications, standards, and problems

Digital Tools in Media Studies

Digital tools are increasingly used in media studies, opening up new perspectives for research and analysis, while creating new problems at the same time. In this volume, international media scholars and computer scientists present their projects, varying from powerful film-historical databases to automatic video analysis software, discussing their application of digital tools and reporting on their results. This book is the first publication of its kind and a helpful guide to both media scholars and computer scientists who intend to use digital tools in their research, providing information on applications, standards, and problems

Cinema Server = s/t (story over time) : an interface for interactive motion picture design

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1993.Includes bibliographical references (leaves 146-148).by Stephan J. Fitch.M.S

Learning-Based Video Highlights Extraction

因為戲劇節目中富含情感的段落往往是最吸引觀眾的部分,所以利用情緒為 基礎的精采片段擷取系統對於戲劇影片檢索和預告片生成是相當有助益的。在本 篇論文當中,我們將精采片段擷取公式化成迴歸的問題,並利用迴歸理論預測影 片片段引發觀眾情感的程度。有別於一般系統從實驗性的觀察中定義試誤性的規 則,本系統利用機器學習決定精采片段與影音特徵的關係。此外,我們從心理學 和戲劇學的角度分析戲劇節目的特性以提出與精采片段相關的影音特徵:人臉、 音樂情緒、鏡頭長度、動作幅度。最後,我們利用量化的方式分析本系統在精采 片段擷取上的準確度。Emotion-based highlights extraction is useful for retrieval and automatic trailer generation of drama video because the rich emotion part of a drama video is often the center of attraction to the viewer. In this thesis, we formulate highlights extraction as a regression problem to extract highlight segments and to predict how strong the viewer’s emotion would be evoked by the video segments. Unlike conventional rule-based approaches that rely on heuristics, the proposed system determines the relation between drama highlights and audiovisual features by machine learning. We also examine the special characteristics of drama video and propose human face, music emotion, shot duration, and motion magnitude as feature sets for highlights extraction. Quantitative evaluation results are provided to illustrate the performance of the system