Adaptive Edge-Oriented Shot Boundary Detection

Adjeroh, Don; Lee, M. C.; Banda, Ntombikayise; Kandaswamy, Umasankar

doi:10.1155/2009/859371

Cited by 57 publications

(21 citation statements)

References 24 publications

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“…It is clear that even the lower bounds for the parameters are much higher than the value of corresponding parameter for other systems. For comparison, we have also worked with a common set of video sequences as used in [1] and these are taken from TRECVID 2001 dataset. System of Adjeroh et al [1] follows an adaptive approach based on edge maps and it outperformed number of systems as presented in their work.…”

Section: Resultsmentioning

confidence: 99%

“…For comparison, we have also worked with a common set of video sequences as used in [1] and these are taken from TRECVID 2001 dataset. System of Adjeroh et al [1] follows an adaptive approach based on edge maps and it outperformed number of systems as presented in their work. Result on the same dataset is also presented in [18].…”

Section: Resultsmentioning

confidence: 99%

“…Motion based features have also been tried [2,11,19]. Many researchers have dealt with edge and gradient based features [1,12,15,25].…”

Section: Past Workmentioning

confidence: 99%

“…Comparison of shot boundary detection performance in terms of F-measure(all figures in scale 0-1) on the dataset used in[1] …”

mentioning

confidence: 99%

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A shot detection technique using linear regression of shot transition pattern

Dutta¹,

Saha

Chanda

2014

Multimed Tools Appl

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Video segmentation acts as the fundamental step for various applications like, archiving, content based retrieval, copy detection and summarization of video data. Shot detection is first level of segmentation. In this work, a shot detection methodology is presented that evolves around a simple shot transition model based on the similarity of the frames with respect to a reference frame. Frames in an individual shot are very similar in terms of their visual content. Whenever a shot transition occurs a change in similarity values appears. For an abrupt transition, the rate of change is very high, while for gradual it is not so apparent. To overcome the effect of noise in similarity values, line is fit over a small window using a linear regression. Thus slope of this line exhibits the underlying pattern of transition. A novel algorithm for shot detection, hence, is developed based on the variation pattern of the similarity values of the frames with respect to a reference frame. First an algorithm is proposed, which is direct descendant of the underlying transition model and applies a threshold on the similarity values to detect the transitions. Then this algorithm is improved by utilizing the slope of linear approximation of variation in similarity values rather than the absolute values, following least square regression. Threshold on the slope is determined with a bias towards minimizing false rejection rate at the cost of false acceptance rate. Finally, a simple post-processing technique is adopted to reduce the false detection. Experiment is done with the video sequences taken from TRECVID 2001 database, action type movie video, recorded sports and news video. Comparison with few other systems indicates that the performance of the proposed scheme is quite satisfactory.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A shot detection technique using linear regression of shot transition pattern

Dutta¹,

Saha

Chanda

2014

Multimed Tools Appl

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show abstract

“…We first divide each video into a number of shots by analyzing shot boundaries for the input videos. Existing shot boundary detection algorithms use various features to analyze the shot boundaries, such as color histogram [32], interest point analysis [33], temporal changes of edges [34], scale invariant transforms [35], and motion [36]. Any algorithm can be adopted in the preprocessing step.…”

Section: A Video Preprocessingmentioning

confidence: 99%

Dynamic Media Assemblage

Luo

Tsai

Chen³

et al. 2013

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-This paper presents dynamic media assemblage, a new presentation and summarization method for images and videos on a 2D canvas. Instead of using the keyframes of the videos to generate a still image summarization, our method allows the videos to play simultaneously on the canvas while utilizing the limited space efficiently. This technique uses an efficient iterative packing algorithm, and as a result is well-suited for interactive manipulations of media files within the assemblages in real-time, such as insertion, deletion, and rearrangement. Our method starts by detecting shot boundaries and dividing longer input videos into individual shots. Within each shot, its temporalspatial salient regions are extracted and used to recover camera motions. These saliency information further defines important regions within individual videos or images, which allows us to preserve visually important regions while packing the media files more efficiently into media assemblages. Our algorithm is iterative and can therefore quickly adjust to new canvas sizes or other user intentions. We demonstrate the effectiveness of our techniques by applying our methods to several applications, including media collection presentation, single video dynamic summary, personal media file browser, and interactive video wall.

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Shot Boundary Detection for Automatic Video Analysis of Historical Films

Helm

Kampel

2019

New Trends in Image Analysis and Processing – ICIAP 2019

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Adaptive Edge-Oriented Shot Boundary Detection

Cited by 57 publications

References 24 publications

A shot detection technique using linear regression of shot transition pattern

A shot detection technique using linear regression of shot transition pattern

Dynamic Media Assemblage

Shot Boundary Detection for Automatic Video Analysis of Historical Films

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