Summarizing video datasets in the spatiotemporal domain

Stefanidis, Anthony; Partsinevelos, Panagiotis; Agouris, Peggy; Doucette, Peter

doi:10.1109/dexa.2000.875134

Cited by 27 publications

(20 citation statements)

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“…Mosaic images, due to the fact that they summarize the static scene content of entire clips, can facilitate browsing and retrieval on the basis of scene appearance, while object tracks can facilitate access on the basis of activity; in [4] a scheme is developed for summarizing video content using mosaics and moving object trajectories, and an approach exploiting the scheme for content-based retrieval and scene visualization is proposed. The problem of analyzing and managing complex dynamic scenes captured in a video sequence is addressed in [5], where the approach followed to summarize video datasets is based on the analysis of the trajectories of objects within them. In order to highlight the importance of visual material for exploiting foreign broadcast news and illustrating the utility of video browsing and summarization interfaces for comparing and contrasting viewpoints across cultural and language boundaries, in [6] systems and tools are developed to automatically detect, extract, and report high interest people, patterns, and trends in visual content from foreign news.…”

Section: Previous Workmentioning

confidence: 99%

Estimation of the Hierarchical Structure of a Video Sequence Using MPEG-7 Descriptors and GCS

Saberi

Carrato

Koprinska

et al. 2005

Lecture Notes in Computer Science

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Abstract. We present a clustering approach for video summarization and browsing based on the Growing Cell Structures (GCS) neural algorithms and MPEG-7 video descriptors. Each I frame is represented as a feature vector of MPEG-7 descriptors. These vectors are clustered using GCS to select one or more keyframes for each shot. The extracted keyframes are then grouped together by TreeGCS to form a hierarchical view of the video for efficient browsing. We evaluate the effectiveness of different combinations of MPEG-7 descriptors and also compare them with the performance of color histograms.

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Section: Previous Workmentioning

confidence: 99%

Estimation of the Hierarchical Structure of a Video Sequence Using MPEG-7 Descriptors and GCS

Saberi

Carrato

Koprinska

et al. 2005

Lecture Notes in Computer Science

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“…They compare algorithms based on heuristics abour slide transitions, pitch information, and user access patterns. Stefanidis et al 11 propose a similar system to that of DeMenthon et al 9 ; however, they split the 3-dimensional trajectories of video objects instead of feature trajectories. The system proposed by Lienhart 14 is designed to summarize home video using clustering based on time stamps.…”

Section: 120mentioning

confidence: 99%

<title>Automated video summarization using speech transcripts</title>

et al. 2001

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Compact representations of video data can enable efficient video browsing. Such representations provide the user with information about the content of the particular sequence being examined while preserving the essential message. We propose a method to automatically generate video summaries for long videos. Our video summarization approach involves mainly two tasks: first, segmenting the video into small, coherent segments and second, ranking the resulting segments. Our proposed algorithm scores segments based on word frequency analysis of speech transcripts. Then a summary is generated by selecting the segments with the highest score to duration ratios and these are concatenating them. We have designed and performed a user study to evaluate the quality of summaries generated. Comparisons are made using our proposed algorithm and a random segment selection scheme based on statistical analysis of the user study results. Finally we discuss various issues that arise in summary evaluation with user studies.

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“…• 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%

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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Summarizing video datasets in the spatiotemporal domain

Cited by 27 publications

References 10 publications

Estimation of the Hierarchical Structure of a Video Sequence Using MPEG-7 Descriptors and GCS

Estimation of the Hierarchical Structure of a Video Sequence Using MPEG-7 Descriptors and GCS

<title>Automated video summarization using speech transcripts</title>

Exploring video content structure for hierarchical summarization

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