Optimising video summaries for mobile devices using visual attention modelling

In order to represent large video collections in the form of key-frame summary on small screen devices, this paper exploits methodology of the visual attention modelling and rapid serial visual presentation. This approach results in an intuitive layout of efficiently generated video summaries. A robust real-time algorithm for key-frame extraction is presented. The system ranks importance of key-frame regions in the final layout by exploiting visual attention modelling. A final layout is created using an optimisation algorithm based on dynamic programming. Algorithm efficiency and robustness are demonstrated by comparing the results with the manually labelled ground truth.

[1]  Andrea Lodi,et al.  Two-dimensional packing problems: A survey , 2002, Eur. J. Oper. Res..

[2]  Gary Marchionini,et al.  Dynamic key frame presentation techniques for augmenting video browsing , 1998, AVI '98.

[3]  P. B. Coaker,et al.  Applied Dynamic Programming , 1964 .

[4]  Robert Spence,et al.  Rapid, Serial and Visual: A Presentation Technique with Potential , 2002, Inf. Vis..

[5]  Oscar de Bruijn,et al.  Rapid serial visual presentation: a space-time trade-off in information presentation , 2000, AVI '00.

[6]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[8]  Neill Campbell,et al.  Comic-like Layout of Video Summaries , 2006 .

[9]  Janko Calic,et al.  Efficient Layout of Comic-Like Video Summaries , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Boon-Lock Yeo,et al.  Rapid scene analysis on compressed video , 1995, IEEE Trans. Circuits Syst. Video Technol..

[11]  David Bull,et al.  Towards Intelligent Content Based Retrieval of Wildlife Videos , 2005 .

[12]  Janko Calic,et al.  Spatial analysis in key-frame extraction using video segmentation , 2004 .

[13]  Dirk Walther,et al.  Interactions of visual attention and object recognition : computational modeling, algorithms, and psychophysics. , 2006 .

[14]  Paul E. Sweeney,et al.  Cutting and Packing Problems: A Categorized, Application-Orientated Research Bibliography , 1992 .

[15]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.