The text segmentation based approaches in natural language processing can be used. The shot boundary detection process for a given video is carried out through two main stages. In the first stage, frames are extracted and labelled with pre-defined labels. In the second stage, the shot boundaries are identified by grouping the labelled frames into segments. We use the following six labels to label frames in a video: NORM FRM (frame of a normal shot), PRE CUT (pre-frame of a CUT transition), POST CUT (post-frame of a CUT transition), PRE GRAD (pre-frame of a GRADUAL transition), IN GRAD (frame inside a GRADUAL transition), and POST GRAD (post-frame of a GRADUAL transition).

Given a sequence of labelled frames, the shot boundaries and transition types are identified by looking up and processing the frames marked with a non NORM FRM label.

Video shot boundary detection using motion activity descriptor

The motion activity is one of the motion features included in the visual part of the MPEG-7 standard. It also used to describe the level or intensity of activity, action, or motion in that video sequence. The main idea underlying the methods of segmentation schemes is that images in the vicinity of a transition are highly dissimilar. It then seeks to identify discontinuities in the video stream. The general principle is to extract a comment on each image, and then define a distance[5] (or similarity measure) between observations. The application of the distance between two successive images, the entire video stream, reduces a one-dimensional signal, in which we seek then the peaks (resp. hollow if similarity measure), which correspond to moments of high dissimilarity.

Representative APR 391%

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