A Classification Approach to Video Shot Boundary Detection
Bose A. Lungisani, Edwin Thuma, Gabofetswe Malema · International Journal of Signal Processing Image Processing and Pattern Recognition · 2017
Video content retrieval just like information retrieval requires some pre-processing such as indexing, key-frame selection and most importantly accurate video shot boundary detection.Accurate detection of video shots give way for video information to be stored in a manner that will allow easy access.Several algorithms have been developed in this field of study and tested even at the TRECVID 2002TRECVID , 2005TRECVID and 2007 tasks evaluation conferences.Challenges on accurate detection of these different types of video transitions have always been from large object and camera motions as well as fast zooming, flashlights, and change in luminance.These attributes differ from one video sequence to another and features of one video sequence cannot always match with features of another video.Therefore, in our work we use a video specific machine learning approach that leverages information from several shot boundary detection algorithms in order to improve the detection of the shot boundaries on a video sequence.Our results suggest that a classifier built from a combination of block-based motion estimation, RGB histogram based block-based cross-correlation coefficient and RGB histogram based sum of squared difference provided better results with an average F1 score of 0.752 on shot boundary detection for the seventeen videos tested.This proves that a combination of luminance and motion based algorithms improves the detection of video shot boundaries.We also found that the detection of shot cuts and gradual transitions can be improved by using features generated by several shot boundary detection algorithms.