DocumentCode :
1809772
Title :
A Novel Video Shot Segmentation Based on Textural Features
Author :
Wang, Yin ; Wen, Xiangming ; Lin, Xinqi ; He, Peizhou ; Zheng, Wei
Author_Institution :
Sch. of Inf. & Commun., Beijing Univ. of Posts & Telecommun., Beijing, China
Volume :
1
fYear :
2009
fDate :
18-20 Aug. 2009
Firstpage :
119
Lastpage :
122
Abstract :
In this paper, a new method for detecting shot boundaries in video sequences using box-counting to extract texture feature and judging the shot boundaries by an improve method to get a dynamic threshold is proposed. Many techniques have been developed to detect the video shot boundaries. But automatic shot boundary detection is difficult. In particular, gradual transitions are generally more difficult to detect. So the paper use the box-counting method to extract the texture feature. Then the paper use the improve method to get the dynamic threshold to detect the shot boundaries. The dynamic threshold is the average frame difference in a slide window multiplied by a threshold coefficient. Experimental results successfully validate the paper method and show that it can effectively detect both the cuts and gradual transition. In particular, the performance is better than others method when the gradual transition shot boundaries are detected.
Keywords :
edge detection; feature extraction; image segmentation; image sequences; image texture; object detection; video signal processing; automatic video shot boundary detection; average frame difference; box-counting method; cut detection; dynamic threshold coefficient method; gradual transition detection; slide window; textural feature extraction; video sequence; video shot segmentation; Cameras; Data mining; Feature extraction; Gunshot detection systems; Indexing; Information security; Motion detection; Object detection; Video sequences; Videoconference;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Assurance and Security, 2009. IAS '09. Fifth International Conference on
Conference_Location :
Xian
Print_ISBN :
978-0-7695-3744-3
Type :
conf
DOI :
10.1109/IAS.2009.232
Filename :
5283500
Link To Document :
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