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Proceedings ArticleDOI

An application for interactive video abstraction

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TLDR
A novel system that can automatically create an optimal and nonrepetitive summarization and support different user requirements for video browsing and content overview by outputting both the optimal set of key frames and a summarized version of the original video with the user-specified time length is proposed.
Abstract
This paper proposes a novel system that can automatically create an optimal and nonrepetitive summarization and support different user requirements for video browsing and content overview by outputting both the optimal set of key frames and a summarized version of the original video with the user-specified time length. Comparing our approach to video abstraction with another algorithm, we demonstrate that our approach is fast and produces an effective video summary.

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Citations
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Journal ArticleDOI

Video abstraction: A systematic review and classification

TL;DR: The purpose of this article is to provide a systematic classification of various ideas and techniques proposed towards the effective abstraction of video contents, and identify and detail, for each approach, the underlying components and how they are addressed in specific works.
Proceedings ArticleDOI

Split-screen dynamically accelerated video summaries

TL;DR: This paper presents a first attempt at automating the evaluation process of the TRECVID 2007 BBC Rushes Summarization task, and describes the processing composed of several steps.
Journal ArticleDOI

Interactive key frame selection model

TL;DR: The model of KFS and IKFS are proved to satisfy the criterion of induction and coinduction, respectively and the processing scheme generates flexible and desirable summarizations whose distortion rate is lower than current method.
Proceedings ArticleDOI

Summarization Extraction Method for Surveillance Video Based on Color Spatial Distribution Characteristic

TL;DR: This paper proposes a novel key frame extraction method by using color spatial distribution histograms for surveillance video representation and summarization using refined histograms to distinguish the small changes of video content better than the original histograms between frames.
Book ChapterDOI

Unsupervised clustering in multimodal multiparty meeting analysis

TL;DR: Nonverbal signals such as gazes, head nods, facial expressions, and bodily gestures play significant roles in organizing human interactions and techniques for meeting event recognitions from nonverbal as well as verbal signals are developed.
References
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Journal ArticleDOI

Rapid scene analysis on compressed video

TL;DR: Experimental results show that the proposed rapid scene analysis algorithms are fast and effective in detecting abrupt scene changes, gradual transitions including fade-ins and fade-outs, flashlight scenes and in deriving intrashot variations.
Journal ArticleDOI

Comparison of video shot boundary detection techniques

TL;DR: This paper presents a comparison of several shot boundary detection and classification techniques and their variations including histograms, discrete cosine transform, motion vector, and block matching methods.
Proceedings ArticleDOI

Adaptive key frame extraction using unsupervised clustering

TL;DR: A new algorithm for key frame extraction based on unsupervised clustering is introduced, both computationally simple and able to adapt to the visual content, which is validated by large amount of real-world videos.
Journal ArticleDOI

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

TL;DR: A novel method for generating key frames and previews for an arbitrary video sequence by first applying multiple partitional clustering to all frames of a video sequence and then selecting the most suitable clustering option(s) using an unsupervised procedure for cluster-validity analysis.
Journal ArticleDOI

A highly efficient system for automatic face region detection in MPEG video

TL;DR: A highly efficient system that can rapidly detect human face regions in MPEG video sequences by detecting faces directly in the compressed domain, and there is no need to carry out the inverse DCT transform, so that the algorithm can run faster than the real time.