Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 573, p.494-506 (2017)

ISBN:

9783319572604

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018713625&doi=10.1007%2f978-3-319-57261-1_49&partnerID=40&md5=e0b62e2e96287cd0c84990cc40801ae3

Keywords:

Clustering techniques, Conformal mapping, Data mining, Empirical studies, entropy, Fuzzy C mean, Gaussian Mixture Model, Intelligent systems, K-means, Self organizing maps, Video recording, Video signal processing, Video summaries, Video summarization, Video summarization system

Abstract:

<p>Identification of relevant frames from a video which can then be used as a summary of the video itself, is a challenging task. An attempt has been made in this study to empirically evaluate the effectiveness of data mining techniques in video summarization. Video Summarization systems based on histogram and entropy features extracted from three different color spaces: RGB, HSV and YCBCR and clustered using K-Means, FCM, GM and SOM were empirically evaluated on fifty video datasets from the VSUMM [1] database. Results indicate that clustering based video summarizations techniques can be effectively used for generating video summaries. © Springer International Publishing AG 2017.</p>

Notes:

cited By 0; Conference of 6th Computer Science On-line Conference, CSOC 2017 ; Conference Date: 26 April 2017 Through 29 April 2017; Conference Code:190739

Cite this Research Publication

A. A. John, Dr. Binoy B. Nair, and Kumar, P. N., “Application of clustering techniques for video summarization - An empirical study”, Advances in Intelligent Systems and Computing, vol. 573, pp. 494-506, 2017.

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