Mining video editing rules in video streams

Matsuo, Yuya; Amano, Miki; Uehara, Kuniaki

doi:10.1145/641043.641058

Cited by 3 publications

(7 citation statements)

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“…1) Adopt Film and TV Editing Patterns: As mentioned in [18], professional video editors use a set of film and TV editing patterns as rules, a so-called video grammar. Depending on the editor's preferences, the edited video may produce a different effect for one piece of raw video source.…”

Section: Discussionmentioning

confidence: 99%

“…Depending on the editor's preferences, the edited video may produce a different effect for one piece of raw video source. Matsuo et al proposed an approach based on data mining to discover editing patterns [18]. This technology, as well as TV and film editing patterns, may be adopted into this system to make the edited result more professional looking.…”

Section: Discussionmentioning

confidence: 99%

“…After a fast Fourier transform (FFT) is performed on each frame of 16 ms long, an octave-scale filter-bank is used to divide the frequency domain into six subbands, including , where refers to the sampling rate. After the amplitude envelope of each subband is extracted by using a half Hamming (raise cosine) window, a Canny operator is used for onset sequence detection by estimating its difference function (17) where is the difference function in the th subband, is the amplitude envelope of the th subband, and is the Canny operator with a Gaussian kernel (18) where is the length of Canny operator and the is used to control the operator's shape, which are set to 12 and 4 in our implementation, respectively.…”

Section: Music Analysismentioning

confidence: 99%

See 2 more Smart Citations

Optimization-Based Automated Home Video Editing System

Hua

Zhang

2004

IEEE Trans. Circuits Syst. Video Technol.

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In this paper, we present an optimization-based system that automates home video editing. This system automatically selects suitable or desirable highlight segments from a set of raw home videos and aligns them with a given piece of incidental music to create an edited video segment to a desired length based on the content of the video and incidental music. We developed an approach for extracting temporal structure and determining the importance of a video segment in order to facilitate the selection of highlight segments. Additionally we extract a temporal structure, beats, and tempos from the incidental music. In order to create more professional-looking results, the selected highlight segments satisfy a set of editing rules and are matched to the content of the incidental music. This task is formulated as a nonlinear 0-1 programming problem and the rules, which are adjustable and increasable, are embedded as constraints. The output video is rendered by connecting the selected highlight video segments with transition effects and the incidental music. Under this framework, we can choose the best-matched music for a given video and support different output styles.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Music Analysismentioning

confidence: 99%

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Optimization-Based Automated Home Video Editing System

Hua

Zhang

2004

IEEE Trans. Circuits Syst. Video Technol.

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“…Watching a video with a fixed focal length can be dull, and more importantly, the focus of lecturing cannot be appropriately delivered, particularly if the focused content is too small to read. To produce a focus-oriented video with the proper rhythm of showing, the aesthetic elements, i.e., the rules and idioms that a videographer usually practices [2], [18] need to be carefully considered in editing. These elements include focal length, view transition and subshot duration.…”

Section: B Aesthetic Considerationsmentioning

confidence: 99%

“…The focal length is to emphasize the degree of interaction, while the selection of focal length is mainly dependent on the underlying action jointly governed by gesture, posture, and focus. To avoid abrupt change of focal length, the view transition and the duration of a subshot can be determined directly based upon the general cinematic rules [2], [18] which outline the basic regulations of placing and connecting subshots for almost all video genres. In addition, to guarantee the smoothness of transitions, various transitional effects including camera motion and cut can be simulated to connect adjacent subshots of different views.…”

Section: B Aesthetic Considerationsmentioning

confidence: 99%

Lecture Video Enhancement and Editing by Integrating Posture, Gesture, and Text

Wang

Ngo

Pong

2007

IEEE Trans. Multimedia

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This paper describes a novel framework for automatic lecture video editing by gesture, posture, and video text recognition. In content analysis, the trajectory of hand movement is tracked and the intentional gestures are automatically extracted for recognition. In addition, head pose is estimated through overcoming the difficulties due to the complex lighting conditions in classrooms. The aim of recognition is to characterize the flow of lecturing with a series of regional focuses depicted by human postures and gestures. The regions of interest (ROIs) in videos are semantically structured with text recognition and the aid of external documents. By tracing the flow of lecturing, a finite state machine (FSM) which incorporates the gestures, postures, ROIs, general editing rules and constraints, is proposed to edit videos with novel views. The FSM is designed to generate appropriate simulated camera motion and cutting effects that suit the pace of a presenter's gestures and postures. To remedy the undesirable visual effects due to poor lighting conditions, we also propose approaches to automatically enhance the visibility and readability of slides and whiteboard images in the edited videos.Index Terms-Gesture, lecture video editing, posture and video text recognition.

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An automated end-to-end lecture capture and broadcasting system

Cha

Rui

Crawford

et al. 2008

ACM Trans. Multimedia Comput. Commun. Appl.

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Remote viewing of lectures presented to a live audience is becoming increasingly popular. At the same time, the lectures can be recorded for subsequent on-demand viewing over the Internet. Providing such services, however, is often prohibitive due to the labor-intensive cost of capturing and pre/post-processing. This article presents a complete automated end-to-end system that supports capturing, broadcasting, viewing, archiving and searching of presentations. Specifically, we describe a system architecture that minimizes the pre-and post-production time, and a fully automated lecture capture system called iCam2 that synchronously captures all contents of the lecture, including audio, video, and presentation material. No staff is needed during lecture capture and broadcasting, so the operational cost of the system is negligible. The system has been used on a daily basis for more than 4 years, during which 522 lectures have been captured. These lectures have been viewed over 20,000 times.

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Mining video editing rules in video streams

Cited by 3 publications

References 0 publications

Optimization-Based Automated Home Video Editing System

Optimization-Based Automated Home Video Editing System

Lecture Video Enhancement and Editing by Integrating Posture, Gesture, and Text

An automated end-to-end lecture capture and broadcasting system

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