Video summarization by clustering using euclidean distance

Sony, Aju; Ajith, Kavya; Kirste, Thomas; Thomas, Tijo; Deepa, P.

doi:10.1109/icsccn.2011.6024630

Cited by 17 publications

(7 citation statements)

References 3 publications

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“…One of the simplest approaches to key frame based segmentation is based on clustering all the frames and then choosing the frame that is best representative of the cluster [1], [2]. Enhancement can include clustering in a perceptual space (i.e.…”

Section: Related Workmentioning

confidence: 99%

A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches

Sukhwani,

Kothari

2017

Preprint

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In this paper, we present a parameterized approach to produce personalized variable length summaries of soccer matches. Our approach is based on temporally segmenting the soccer video into "plays", associating a user-specifiable "utility" for each type of play and using "bin-packing" to select a subset of the plays that add up to the desired length while maximizing the overall utility (volume in bin-packing terms). Our approach systematically allows a user to override the default weights assigned to each type of play with individual preferences and thus see a highly personalized variable length summarization of soccer matches. We demonstrate our approach based on the output of an end-to-end pipeline that we are building to produce such summaries. Though aspects of the overall end-to-end pipeline are human assisted at present, the results clearly show that the proposed approach is capable of producing semantically meaningful and compelling summaries. Besides the obvious use of producing summaries of superior league matches for news broadcasts, we anticipate our work to promote greater awareness of the local matches and junior leagues by producing consumable summaries of them.

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Section: Related Workmentioning

confidence: 99%

A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches

Sukhwani,

Kothari

2017

Preprint

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“…Sony et al in [3] use Euclidean distance after clustering to obratin summarized frames. This method is based on the removal of redundant frames from a video and maintaining the user defined number of unique frames.…”

Section: Related Workmentioning

confidence: 99%

Key Frame Extraction for Video Summarization Using Motion Activity Descriptors

Kamoji¹

2014

IJRET

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Summarization of a video involves providing a gist of the entire video without affecting the semantics of the video. This has been implemented by the use of motion activity descriptors which generate relative motion between consecutive frames. Correctly capturing the motion in a video leads to the identification of the key frames in the video. This motion in the video can be obtained by using block matching techniques which is an important part of this process. It is implemented using two techniques, Diamond Search and Three Step Search, which have been studied and compared. The comparison process is tried across various videos differing in category, content, and objects. It is found that there is a trade-off between summarization factor and precision during the summarization process.

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“…Authors investigate both binary and decimal GA. It is seen that the binary GA finds the optimal result more quickly and easily than the decimal GA. Sony et al (2011) use Euclidean distance after clustering to obtain summarised frames. This method is based on the removal of redundant frames from a video and returns the user defined number of unique frames.…”

Section: Introductionmentioning

confidence: 99%

Video summarisation using optimum global threshold technique based on genetic algorithm

Phadikar

Kumar²,

Phadikar

et al. 2016

IJICA

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Most of the methods for video summarisation rely on complicated clustering algorithms that make them too computationally complex for real time applications. This paper presents an efficient approach for video summary generation that does not relay on complex clustering algorithms and does not require frame length as a parameter. The present scheme combines colour histogram and edge histogram features with optimum global thresholding to detect key frames. The optimum threshold is selected based on genetic algorithm (GA) to increase the performance of the proposed system. For each shot, key frames are extracted and similar key frames are eliminated. Experimental results duly support those claims.

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Video summarization by clustering using euclidean distance

Cited by 17 publications

References 3 publications

A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches

A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches

Key Frame Extraction for Video Summarization Using Motion Activity Descriptors

Video summarisation using optimum global threshold technique based on genetic algorithm

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