Segmentation method

The simplest approach as in color histogram is to compute the differences between the color distributions of consecutive frames and use a threshold to classify whether a hard cut occurs. In order to detect gradual transitions, edge change ratios or motion vectors can be used. Since these approaches use threshold based models for detection, their advantage is they are fast. Nevertheless, they are sensitive to changes in illumination and motion. Furthermore, they are difficult to generalize for new datasets. Recent works use machine learning methods for making decisions and have received impressive results on the test videos.

Given a sequence of labeled frames, the shot boundaries and transition types are identified by looking up and processing the frames marked. To label a frame in a video, we must firstly extract the features for that frame and then use a classifier, which has been trained in advance by the annotated frames, to classify it into one of the six categories 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 (postframe of a GRADUAL transition).

Representative APR 391%

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