The document discusses shot boundary detection in videos using color histograms and feature points. It presents two common methods: using temporal color variation with histograms and using feature point matching between frames. It then provides results from testing the histogram method on a 250GB video database containing over 60 million frames, taking around 4 days to process with a 61% correct detection rate and 39% incorrect detections. The conclusion discusses room for improvement in dissolve shot detection and reducing false detections.

1. Tran Quang Anh
Tran Thuong Khanh
Bui Ngo Da Thao
Pham The Bao
Faculty of Mathematics and Computer Science
University of Science, VNUHCM

2.  Introduction
 Our solution
 Result
 Conclusion

3. Shot A Shot Shot B
boundary

4.  Using histogram
◦ Temporal color variation [1]
 Using Non-histogram
◦ Feature point [2]
1. Jordi Mas, GarielFrenadez,Video shot boundary detection based on color histogram, Proceedings of the TRECVid
Workshop, Gaithersburg, Maryland USA, 2003
2. Yousri Abdeljaoued, Touradj Ebrahimi, Charilaos Christopoulos2and Ignacio Mas Ivars, Proceeding of European
Signal Processing Conference 1998

6. Frame A
Image
One shot
subtraction
Frame A+b
Two shots
Histogram
Comparison

7. Frame A Frame B
Result Image

8. Histogram of frame A Histogram of frame B

9. We test base on database volume : 250GB include many kinds of video
Computer configuration : Core 2 Duo 2.2GHz , 3GB Ram, Win 7.Pro with
Matlab version 2009Ra
Database Number of Time Correct ratio Incorrect
frames ratio
250GB 60 342 857 1462857s 61% 39%
frames

10. Fast and simple solution
High accuracy with abrupt detection and
some dissolve shot detections.
Having some error in dissolve detection .
Having false shot.

11.  Improving redundant detection.
 Building new methods