An Improved Algorithm for Video Summarization - A Rank Based Approach

Manasa Srinivas; M. M.Manohara Pai; Radhika M. Pai

doi:10.1016/j.procs.2016.06.065

An Improved Algorithm for Video Summarization - A Rank Based Approach

Manasa Srinivas, M. M.Manohara Pai^*, Radhika M. Pai

^*Corresponding author for this work

Manipal Institute of Technology, Manipal

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

Video summarization is one of the promising approaches for effective comprehension of video content by selecting informative frames of the video. The aim is to produce a summary of the video which is interesting to the user and representing the whole video. In this paper the proposed approach for video summarization takes various features into account such as representativeness, uniformity, static attention, temporal attention and quality which includes colorfulness, brightness, contrast, hue count, edge distribution for selecting keyframes. Experiments have been conducted on videos from open-video.org and results are compared with the standard ground truth. The results are also compared with the algorithm in literature and is found to produce better results.

Original language	English
Pages (from-to)	812-819
Number of pages	8
Journal	Procedia Computer Science
Volume	89
DOIs	https://doi.org/10.1016/j.procs.2016.06.065
Publication status	Published - 2016
Event	12th International Conference on Communication Networks, ICCN 2016, 12th International Conference on Data Mining and Warehousing, ICDMW 2016 and 12th International Conference on Image and Signal Processing, ICISP 2016 - Bangalore, India Duration: 19-08-2016 → 21-08-2016

All Science Journal Classification (ASJC) codes

General Computer Science

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10.1016/j.procs.2016.06.065

Cite this

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title = "An Improved Algorithm for Video Summarization - A Rank Based Approach",

abstract = "Video summarization is one of the promising approaches for effective comprehension of video content by selecting informative frames of the video. The aim is to produce a summary of the video which is interesting to the user and representing the whole video. In this paper the proposed approach for video summarization takes various features into account such as representativeness, uniformity, static attention, temporal attention and quality which includes colorfulness, brightness, contrast, hue count, edge distribution for selecting keyframes. Experiments have been conducted on videos from open-video.org and results are compared with the standard ground truth. The results are also compared with the algorithm in literature and is found to produce better results.",

author = "Manasa Srinivas and Pai, {M. M.Manohara} and Pai, {Radhika M.}",

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year = "2016",

doi = "10.1016/j.procs.2016.06.065",

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AU - Pai, Radhika M.

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N2 - Video summarization is one of the promising approaches for effective comprehension of video content by selecting informative frames of the video. The aim is to produce a summary of the video which is interesting to the user and representing the whole video. In this paper the proposed approach for video summarization takes various features into account such as representativeness, uniformity, static attention, temporal attention and quality which includes colorfulness, brightness, contrast, hue count, edge distribution for selecting keyframes. Experiments have been conducted on videos from open-video.org and results are compared with the standard ground truth. The results are also compared with the algorithm in literature and is found to produce better results.

AB - Video summarization is one of the promising approaches for effective comprehension of video content by selecting informative frames of the video. The aim is to produce a summary of the video which is interesting to the user and representing the whole video. In this paper the proposed approach for video summarization takes various features into account such as representativeness, uniformity, static attention, temporal attention and quality which includes colorfulness, brightness, contrast, hue count, edge distribution for selecting keyframes. Experiments have been conducted on videos from open-video.org and results are compared with the standard ground truth. The results are also compared with the algorithm in literature and is found to produce better results.

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Y2 - 19 August 2016 through 21 August 2016

ER -

An Improved Algorithm for Video Summarization - A Rank Based Approach

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