Using string matching to detect video transitions

The detection of shot boundaries in videos captures the structure of the image sequences by the identification of transitional effects. This task is important in the video indexing and retrieval domain. The video slice or visual rhythm is a single two-dimensional image sampling that has been used to detect several types of video events, including transitions. We use the longest common subsequence (LCS) between two strings to transform the video slice into one-dimensional signals obtaining a highly simplified representation of the video content. We also developed a chain of mathematical morphology operations over these signals leading to the detection of the most frequent video transitions, namely, cut, fade, and wipe. The algorithms are tested with success with various genres of videos.