A Psychological Adaptive Model For Video Analysis

Extracting key-frames is the first step for efficient content-based indexing, browsing and retrieval of the video data in commercial movies. Most of the existing research deals with "how to extract representative frames?" However the unaddressed question is "how many key-frames are required to represent a video shot properly?" Generally, the user defines this number a priori or some heuristic methods are used. In this paper, we propose a psychological model, which computes this number adaptively and online, from variation of visual features in a video-shot. We incorporate it with an iterative key-frame selection method to automatically select the key-frames. We compare the results of this method with two other well-known approaches, based on a novel effectiveness measure that scores each approach based on its representational power. Movie-clips of varying complexity are used to underscore the success of the proposed model in real-time

[1]  Wu Zhong.  International Trends of Pattern Recognition Research A Brief Introduction to the 18th International Conference on Pattern Recognition , 2006 .

[2]  S. Soraci,et al.  Visual Information Processing , 2003 .

[3]  F. O. Huck,et al.  Visual Information Processing , 1992 .

[4]  Minerva M. Yeung,et al.  Efficient matching and clustering of video shots , 1995, Proceedings., International Conference on Image Processing.

[5]  Marc M. Van Hulle,et al.  A phase-based approach to the estimation of the optical flow field using spatial filtering , 2002, IEEE Trans. Neural Networks.

[6]  Mubarak Shah,et al.  Scene detection in Hollywood movies and TV shows , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[7]  Ramin Zabih,et al.  A feature-based algorithm for detecting and classifying scene breaks , 1995, MULTIMEDIA '95.

[8]  Wayne H. Wolf,et al.  Key frame selection by motion analysis , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[9]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[10]  Seong-Dae Kim,et al.  Iterative key frame selection in the rate-constraint environment , 2003, Signal Process. Image Commun..

[11]  P. Subramanian.  Active Vision: The Psychology of Looking and Seeing , 2006 .

[12]  Allen Allport,et al.  Visual attention , 1989 .

[13]  R. Haber,et al.  The psychology of visual perception , 1973 .

[14]  Yueting Zhuang,et al.  Adaptive key frame extraction using unsupervised clustering , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).