Publication Type : Journal Article
Publisher : Advances in Intelligent Systems and Computing, Springer Verlag
Source : Advances in Intelligent Systems and Computing, Springer Verlag, Volume 573, p.494-506 (2017)
ISBN : 9783319572604
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
Campus : Coimbatore
School : School of Engineering
Department : Computer Science, Electrical and Electronics
Verified : Yes
Year : 2017
Abstract : 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.
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.