More and more home videos are being generated with the ever growing popularity of digital cameras and camcorders. For many home videos, a photo rendering, whether capturing a moment or a scene within the video, provides a complementary representation to the video. In this paper, a video motion mining framework for creative rendering is presented. The user-s capture intent is derived by analyzing video motions, and respective metadata is generated for each capture type. The metadata can be used in a number of applications, such as creating video thumbnail, generating panorama posters, and producing slideshows of video.
 J. Bergen, P. Anandan, and K. Hanna, "Hierarchical model-based motion
estimation", ECCV, 1992.J. Park, N. Yagi, K. Enami, K. Aizawa, and M.
Hatori, "Estimatino of camera parameters from image sequence for
model based video coding", IEEE Trans. On Circuit and System for
Video Technology. Vol. 4, No. 3, June 1994.
 R. L. Rardin, Optimization in Operations Research, Prentice Hall, 1998,
 M. Chen, "Dynamic Content adaptive super-resolution", Int. Conf.
Image Analysis and Recognition, Sept. 2003.
 (Polana92) Ramprasad Polana and Randal C. Nelson, Recognition of
Motion from Temporal Texture, Proc. IEEE Conference on Computer
Vision and Pattern Recognition, Champaign, Illinois, June 1992, 129-
 (Kim97) Eung Tae Kim, Jong Ki Han, Hyung-Myung Kim, "A Kalmanfiltering
method for 3D camera motion estimation from image
sequences," Proceedings of ICIP 97, vol. 3, pp. 630-633, Oct. 1997.