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Journal ArticleDOI

Video abstraction: A systematic review and classification

TLDR
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.
Abstract
The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information, while at the same time providing user-friendly access to the stored data. This has fueled a quickly evolving research area known as video abstraction. As the name implies, video abstraction is a mechanism for generating a short summary of a video, which can either be a sequence of stationary images (keyframes) or moving images (video skims). In terms of browsing and navigation, a good video abstract will enable the user to gain maximum information about the target video sequence in a specified time constraint or sufficient information in the minimum time. Over past years, various ideas and techniques have been proposed towards the effective abstraction of video contents. The purpose of this article is to provide a systematic classification of these works. We identify and detail, for each approach, the underlying components and how they are addressed in specific works.

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Citations
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Journal ArticleDOI

VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method

TL;DR: VSUMM is presented, a methodology for the production of static video summaries that is based on color feature extraction from video frames and k-means clustering algorithm and develops a novel approach for the evaluation of video static summaries.
Journal ArticleDOI

A Survey on Visual Content-Based Video Indexing and Retrieval

TL;DR: Methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, and video retrieval including query interfaces are analyzed.
Proceedings ArticleDOI

TVSum: Summarizing web videos using titles

TL;DR: A novel co-archetypal analysis technique is developed that learns canonical visual concepts shared between video and images, but not in either alone, by finding a joint-factorial representation of two data sets.
Book ChapterDOI

Superpixels and supervoxels in an energy optimization framework

TL;DR: This work forms the superpixel partitioning problem in an energy minimization framework, and explores variations of the basic energy, which allow a trade-off between a less regular tessellation but more accurate boundaries or better efficiency.
Proceedings ArticleDOI

Video summarization by learning submodular mixtures of objectives

TL;DR: A new method is introduced that uses a supervised approach in order to learn the importance of global characteristics of a summary and jointly optimizes for multiple objectives and thus creates summaries that posses multiple properties of a good summary.
References
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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.
Proceedings ArticleDOI

A user attention model for video summarization

TL;DR: A generic framework of video summarization based on the modeling of viewer's attention is presented, which takes advantage of computational attention models and eliminates the needs of complex heuristic rules inVideo summarization.
Proceedings ArticleDOI

Automatically extracting highlights for TV Baseball programs

TL;DR: This paper explores how to provide for the ability to extract highlights automatically, so that viewing time can be reduced, and presents results comparing output of algorithms against human-selected highlights for a diverse collection of baseball games with very encouraging results.
Journal ArticleDOI

An integrated system for content-based video retrieval and browsing

TL;DR: These processes and a set of tools to facilitate content-based video retrieval and browsing using the feature data set are presented in detail as functions of an integrated system.
Proceedings ArticleDOI

Key frame selection by motion analysis

TL;DR: This paper describes a new algorithm which uses optical flow computations to identify local minima of motion in a shot-stillness emphasizes the image for the viewer.
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