Image similarity for automatic video summarization

Image similarity is a key issue for many multimedia applications. Video summarization is no exception. We have recently proposed a number of methodologies for creating visually significant summaries of videos. Our approach relies heavily on the metric which decides on whether two video key-frames are similar or not. In this paper, we compare a number of histogram representations and possible distance measures with the objective of improving the quality of video summaries.

[1]  Wolfgang Effelsberg,et al.  Video abstracting , 1997, CACM.

[2]  Shingo Uchihashi,et al.  Summarizing video using a shot importance measure and a frame-packing algorithm , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[3]  C. Tomasi.  The Earth Mover's Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval , 1997 .

[4]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[5]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Brian V. Funt,et al.  Color Constant Color Indexing , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Bernard Mérialdo,et al.  Generating summaries of multi-episode video , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

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

[9]  Takeo Kanade,et al.  Video skimming and characterization through the combination of image and language understanding , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[10]  Xin Liu,et al.  Generating optimal video summaries , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[11]  T. John Stonham,et al.  Content-based image retrieval using color tuple histograms , 1996, Electronic Imaging.

[12]  Nuno Vasconcelos,et al.  Bayesian modeling of video editing and structure: semantic features for video summarization and browsing , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[13]  Peter J. L. van Beek,et al.  Image retrieval using blob histograms , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).