Unsupervised Video Summarization via Dynamic Modeling-Based Hierarchical Clustering

Mining the video data using unsupervised learning techniques can reveal important information regarding the internal visual content of large video databases. One of these information is the video summary which is a sequence of still pictures that represent the content of a video in such a way that the respective target group is rapidly provided with concise information about the content, while the essential message of the original video is preserved. In this paper, an enhanced method for generating static video summaries is presented. This method utilizes a modified dynamic modeling-based hierarchical clustering algorithm that depends on the temporal order and sequential nature of the video to fasten the clustering process. Video summaries generated by our method are compared with summaries generated by others found in the literature and the ground truth summaries. Experimental results indicate that the video summaries generated by the proposed method have a higher quality than others.

[1]  Arjen P. de Vries,et al.  Multimedia Retrieval (Data-Centric Systems and Applications) , 2007 .

[2]  Ba Tu Truong,et al.  Video abstraction: A systematic review and classification , 2007, TOMCCAP.

[3]  Marco Pellegrini,et al.  STIMO: STIll and MOving video storyboard for the web scenario , 2009, Multimedia Tools and Applications.

[4]  Yelena Yesha,et al.  Keyframe-based video summarization using Delaunay clustering , 2006, International Journal on Digital Libraries.

[5]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[6]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[7]  Nagia M. Ghanem,et al.  VSCAN: An Enhanced Video Summarization Using Density-Based Spatial Clustering , 2013, ICIAP.

[8]  Silvio Jamil F. Guimarães,et al.  A Static Video Summarization Method Based on Hierarchical Clustering , 2010, CIARP.

[9]  Arnaldo de Albuquerque Araújo,et al.  VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method , 2011, Pattern Recognit. Lett..

[10]  Neil A. Thacker,et al.  The Bhattacharyya metric as an absolute similarity measure for frequency coded data , 1998, Kybernetika.

[11]  T. Kailath.  The Divergence and Bhattacharyya Distance Measures in Signal Selection , 1967 .

[12]  David S. Doermann,et al.  Video summarization by curve simplification , 1998, MULTIMEDIA '98.

[13]  Wolfgang Effelsberg,et al.  Abstracting Digital Movies Automatically , 1996, J. Vis. Commun. Image Represent..

[14]  Vipin Kumar,et al.  Hmetis: a hypergraph partitioning package , 1998 .

[15]  Arnaldo de Albuquerque Araújo,et al.  Video segmentation based on 2D image analysis , 2003, Pattern Recognit. Lett..

[16]  Mario A. Nascimento,et al.  Techniques for Color-Based Image Retrieval , 2003 .