Shot-based similarity measure for content-based video summarization

Gao, Yue; Dai, Qionghai

doi:10.1109/icip.2008.4712304

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Storyboard can be implemented by extracting a set of key-frames from original videos to represent the overview of the videos. A framework for scalable representation of videos using an iterative ranking procedure to adapt the summaries to a suitable length is proposed in [3]. In [4] authors analyzed the location of home videos and sitcoms with MST-based clustering and energy function.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Mining of Visually and Temporally Consistent Shots for Sports Video Summarization

Dong

Zhang

Zhao

et al. 2013

AMM

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Video summarization aims at providing compact representation containing enough information for users to understand the entire content or important events, which serves as the fundamental process in content-based video analysis. This paper presents a novel sport video summarization algorithm by mining consistent field-of-views applied visual and temporal information in a totally unsurprised manner. After videos are broken into shots, a content-based similarity measure is proposed in the shot level to structurally analysis the visually matching cost of original videos. Then modified agglomerative hierarchical clustering is performed with an energy-based function to match the statistical distribution of various views in game videos and a refined distance metric is proposed as similarity measure of two shots. Extended temporal prior is introduced to meet the fact that temporally neighbored shots with similar duration are more likely to be in the same clusters. Experiments on a database of 6 sport genres with over 10251 minutes of videos from different sources achieved an average accuracy of 91.5% and quantitative results are presented to justify each choice made in the design of our algorithm. Our proposed algorithm is applied for the non-linear browsing service of Orangesports by France Telecomm and an android based app has been implemented for smart mobile devices.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Mining of Visually and Temporally Consistent Shots for Sports Video Summarization

Dong

Zhang

Zhao

et al. 2013

AMM

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“…Clustering of video shots also has been applied to understand associated semantics in video organization which can lead to detecting of scenes in the video (Rasheed & Shah, 2005). Furthermore a wide range of other video-related applications from content-based annotation of video to video summarization can benefit from effective clustering of similar shots (Gao & Dai, 2008). Generally speaking, the different shot clustering approaches utilize either all frames in a video (Chen, Wang, & Wang, 2009;Zhang, Sun, Yang, & Zhong, 2005) or only a particular frame representing the shot, called the keyframe (Odobez, Gatica-Perez, & Guillemot, 2003) as the initial unit of video.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification

Rajan

Younessian

2011

International Journal of Multimedia Data Engineering and Management

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In this paper, the authors propose an effective content-based clustering method for keyframes of news video stories using the Near Duplicate Keyframe (NDK) identification concept. Initially, the authors investigate the near-duplicate relationship, as a content-based visual similarity across keyframes, through the Near-Duplicate Keyframe (NDK) identification algorithm presented. The authors assign a near-duplicate score to each pair of keyframes within the story. Using an efficient keypoint matching technique followed by matching pattern analysis, this NDK identification algorithm can handle extreme zooming and significant object motion. In the second step, the weighted adjacency matrix is determined for each story based on assigned near duplicate score. The authors then use the spectral clustering scheme to remove outlier keyframes and partition remainders. Two sets of experiments are carried out to evaluate the NDK identification method and assess the proposed keyframe clustering method performance.

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News videos anchor person detection by shot clustering

Cao

Zhang

et al. 2014

Neurocomputing

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Shot-based similarity measure for content-based video summarization

Cited by 7 publications

References 8 publications

Unsupervised Mining of Visually and Temporally Consistent Shots for Sports Video Summarization

Unsupervised Mining of Visually and Temporally Consistent Shots for Sports Video Summarization

Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification

News videos anchor person detection by shot clustering

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