In the first step of this process, feature extraction is performed, where the features depict various aspects of the visual content of a video. Then, a metric is used to quantify the feature variation from k frame to frame k+l. The discontinuity value z(k, k+l) is the magnitude of this variation and serves as an input into the detector. There, it is compared against a threshold T. If the threshold is exceeded, a shot boundary between frames k and k+l is detected. To be able to draw reliable conclusions about the presence or absence of a shot boundary between frames k and k+l , we need to use the features and metrics for computing the discontinuity values z(k, k+l) that are as discriminating as possible.

This means that a clear separation should exist between discontinuity-value ranges for measurements performed within shots and at shot boundaries.

Further in this paper section 2, shows the different features that can be used to measure visual discontinuity, section 3, shows the method use to implement video shot boundary detection algorithm, section 4, gives a comparison between between difference methods.

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