Entropy metrics used for video summarization

Cernekova, Zuzana; Nikou, Christophoros; Pitas, Ioannis

doi:10.1145/584458.584471

Cited by 17 publications

(16 citation statements)

References 18 publications

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“…Other systems, such asČerneková et al [10], do not take into account the inter-shot relationship in which our proposed system handles in the post-processing stage.…”

Section: Resultsmentioning

confidence: 99%

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Particle swarm optimisation based video abstraction

Fayk

Nemr

Moussa

2010

Journal of Advanced Research

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Video abstraction is a basic step for intelligent access to video and multimedia databases which facilitates content-based video indexing, retrieving and browsing. This paper presents a new video abstraction scheme. The proposed method relies on two stages. First, video is divided into short segments. Second, keyframes in each segment are selected using particle swarm optimisation. A group of experiments show that the proposed technique is promising in regards to selecting the most significant keyframes despite a sustainment in overhead processing.

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“…Other systems, such asČerneková et al [10], do not take into account the inter-shot relationship in which our proposed system handles in the post-processing stage.…”

Section: Resultsmentioning

confidence: 99%

“…Video segmentation has been performed using the colour distribution of frames [7,10], edges [7], or motion [11,12]. In previous work of the authors [13] segmentation was performed using edge change ratio (ECR) as well as the colour of the frames, followed by keyframe selection using PSO.…”

Section: Video Segmentationmentioning

confidence: 99%

Particle swarm optimisation based video abstraction

Fayk

Nemr

Moussa

2010

Journal of Advanced Research

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“…Entropy has been proposed as a metric for image segmentation in [6,8,10], and for the detection of scene boundaries in [2]. although that work does not consider the burden of proof required for our application.…”

Section: Independent Luminance Entropymentioning

confidence: 99%

Video Motion Detection Beyond Reasonable Doubt

Xiao¹,

Poursoltanmohammad²,

Sorell³

2008

Proceedings of the 1st International ICST Conference on Forensic Applications and Techniques in Telecommunications, Information

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We consider the analysis of surveillance video footage containing occasional activities of potential interest interspersed with long periods of no motion. Such evidence is problematic for three reasons: firstly, it takes up a great deal of storage capacity with little evidential value; secondly, human review of such surveillance is extremely time-consuming and subject to errors due to fatigue; and thirdly, there is often a need to prove to the satisfaction of the Court that excised footage contains no images of evidential value. We are therefore concerned with objective, reliable detection of video motion to automate the extraction of activities of interest and to provide simple but reliable measurements to the court to prove that this is a complete record of all activities in the footage. Early results indicate that average luminance-based detection is particularly reliable, and we provide a comparison with other frame-difference techniques.

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“…Certain color histogram-based algorithms are developed for detecting video cuts [17][18][19][20] and fades. [21][22][23][24][25] Our method is similar to a recent video cut and fade detection algorithm by Z. Č erneková et al 26 which is based on entropy metrics of mutual information and joint entropy.…”

Section: Related Workmentioning

confidence: 99%

Entropy‐based motion extraction for motion capture animation

So¹,

Baciu

2005

Computer Animation & Virtual

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In this paper, we present a new segmentation solution for extracting motion patterns from motion capture data by searching for critical keyposes in the motion sequence. A rank is established for critical keyposes that identifies the significance of the directional change in motion data. The method is based on entropy metrics, specifically the mutual information measure. Displacement histograms between frames are evaluated and the mutual information metric is employed in order to calculate the inter-frame dependency. The most significant keypose identifies the largest directional change in the motion data. This will have the lowest mutual information level from all the candidate keyposes. Less significant keyposes are then listed with higher mutual information levels. The results show that the method has higher sensitivity in the directional change than methods based on the magnitude of the velocity alone. This method is intended to provide a summary of a motion clip by ranked keyposes, which is highly useful in motion browsing and motion retrieve database system.

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Entropy metrics used for video summarization

Cited by 17 publications

References 18 publications

Particle swarm optimisation based video abstraction

Particle swarm optimisation based video abstraction

Video Motion Detection Beyond Reasonable Doubt

Entropy‐based motion extraction for motion capture animation

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