Videoabstract: a hybrid approach to generate semantically meaningful video summaries

Toklu, C.; Liou, Shih-Ping; Das, M.

doi:10.1109/icme.2000.871012

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…Many algorithms believe that the scenario (such as a story unit) is more suitable for summarization [3,30,31,42] than others, where video scenarios are detected using timeconstrained clustering [3], speaker or subject change detection [30,31,42], etc. The summary is constructed using the representative frames (or shots) of the story units in the form of a storyboard or a skimming.…”

Section: Related Workmentioning

confidence: 99%

“…Since most research assumes the video summary is generated for efficient browsing, indexing, and navigating, the pictorial summary is popularly used to visualize the video summary via icon frames [3,7,[11][12][13]25,30,31], mosaic images [18,19,34], video objects [8][9][10], or a data distribution map [20][21][22]. A curve-simplification video-summarization strategy is introduced in [12], which maps each frame into a vector of high-dimensional features and segments the feature curve into units.…”

Section: Related Workmentioning

confidence: 99%

“…• A pictorial summary [3,[7][8][9][10][11][12][13]18,19,25,30,31] is a collection of still images (possibly varying in size) arranged in time. The use of compact images provides the user with an overview of the video directly and efficiently; however, the motion and audio information of the video is lost.…”

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confidence: 99%

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Exploring video content structure for hierarchical summarization

Zhu

Fan

et al. 2004

Multimedia Systems

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In this paper, we propose a hierarchical video summarization strategy that explores video content structure to provide the users with a scalable, multilevel video summary. First, video-shot-segmentation and keyframe-extraction algorithms are applied to parse video sequences into physical shots and discrete keyframes. Next, an affinity (self-correlation) matrix is constructed to merge visually similar shots into clusters (supergroups). Since video shots with high similarities do not necessarily imply that they belong to the same story unit, temporal information is adopted by merging temporally adjacent shots (within a specified distance) from the supergroup into each video group. A video-scene-detection algorithm is thus proposed to merge temporally or spatially correlated video groups into scenario units. This is followed by a scene-clustering algorithm that eliminates visual redundancy among the units.A hierarchical video content structure with increasing granularity is constructed from the clustered scenes, video scenes, and video groups to keyframes. Finally, we introduce a hierarchical video summarization scheme by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary. Extensive experiments based on real-world videos have been performed to validate the effectiveness of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Exploring video content structure for hierarchical summarization

Zhu

Fan

et al. 2004

Multimedia Systems

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“…Audio features such as zero crossing rate have been used to detect and represent certain characteristics (e.g., speechmusic discriminator) [39]. More sophisticated feature extraction and classification methods have also been used quite successfully [22].…”

Section: Image Analysismentioning

confidence: 99%

Multimodal detection of highlights for multimedia content

Dağtaş

Abdel-Mottaleb

2004

Multimedia Systems

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We present a multimedia information analysis framework for content-based browsing of video. Specifically, we develop algorithms for the automated extraction of video highlights in sports video that are based on audio, text, and image features. The extracted annotations are used to build applications for selective browsing of sports videos. Such summarization techniques enable content-based indexing of multimedia documents for efficient storage and retrieval. In addition, in the context of the newly emerging standard MPEG-7, these methods will enable applications that use MPEG-7 descriptions. As this standard provides only the syntax for representing such descriptions and not specific algorithms for extracting them, these algorithms are of great value for establishing MPEG-7 as an accepted standard. We provide experimental results for the proposed algorithms on several hours of sports programs that prove the feasibility of efficient video access techniques in a multimedia environment.

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“…Past work on automatic extraction of highlights of sports events in general and soccer video in particular has relied on color feature extraction, audio feature extraction as well as camera motion extraction (see [1] [2] for example). Color and texture features have been employed to find the video segments in which the background is mostly green i.e.…”

Section: Introductionmentioning

confidence: 99%

Automatic extraction of soccer video highlights using a combination of motion and audio features

Cabasson

Divakaran

2003

SPIE Proceedings

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We present a technique for rapidly generating highlights of soccer videos using peaks in audio volume in conjunction with temporal patterns of motion activity extracted in the compressed domain. Our intuition is that any interesting event, such as a goal, in a soccer match leads to an interruption of the game for a non-trivial duration. Furthermore, interesting events are associated with a sharp increase (or peak) in audio volume since the crowd noise goes up in anticipation of the event or as a result of the event. We thus use the temporal patterns of motion activity around each audio peak to detect and capture interesting events. Our preliminary results indicate that the scheme works well for a variety of soccer content from different parts of the world. The computational simplicity of our scheme enables rapid and flexible generation of highlights.

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Videoabstract: a hybrid approach to generate semantically meaningful video summaries

Cited by 11 publications

References 15 publications

Exploring video content structure for hierarchical summarization

Exploring video content structure for hierarchical summarization

Multimodal detection of highlights for multimedia content

Automatic extraction of soccer video highlights using a combination of motion and audio features

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