  The goal of video segmentation is to turn video data into a set of concrete
motion clusters that can be easily interpreted as building blocks of the video.
There are some works on similar topics like detecting scene cuts in a video,
but there is few specific research on clustering video data into the desired
number of compact segments. It would be more intuitive, and more efficient, to
work with perceptually meaningful entity obtained from a low-level grouping
process which we call it superframe. This paper presents a new simple and
efficient technique to detect superframes of similar content patterns in
videos. We calculate the similarity of content-motion to obtain the strength of
change between consecutive frames. With the help of existing optical flow
technique using deep models, the proposed method is able to perform more
accurate motion estimation efficiently. We also propose two criteria for
measuring and comparing the performance of different algorithms on various
databases. Experimental results on the videos from benchmark databases have
demonstrated the effectiveness of the proposed method.
