Image Processing Reference
The function affects the weighted contribution of each frame into the velocity of the segment
in an angle interval according to whether its vectors' are noisy or not.
As it is mentioned before, the weighted frame velocity is, now, a new component of the fea-
ture vector representation based on the movement of the segment. Thus, the new representa-
tion is as follows:
Now, the operator Φ in the generic optical-based representation model R = [ S ( V ), Φ ] is
deined in this specific problem. The operator maps the optical flow vector set S ( V ) to the fea-
ture vector R for a video scene.
The function of the above mapping is shown in the obtained final representation. Mainly, it
constructs the representation by applying the operator to the optical flow vectors. The operat-
or Φ , in fact, is the symbolic representation of our method.
The practical use of the representation is classifying the segments. The representation is
used for each video segment and has the size m x2. The segment classification, constant estim-
ations, and experiments with results and comparisons will be held in Section 7 .
6 Cut Detection Inspiration
Temporal video segmentation is the problem of spliting the video information temporally
into coherent scenes. Temporal video segmentation is generally originated from the needs of
video segment classification. As it is needed in our study, the video scenes are needed to be ex-
tracted from whole video information in many cases in order to classify them semantically as
segments. On the other hand, in some cases, video segment classification and temporal video
segmentation are held together. This kind of methods tackles the problem with an integrated
approach by trying to carry out the scene extraction with the classification of related semantic
As temporal video information is composed of visually complicated and continuous se-
quence of video frames, analyzing the temporal boundaries of video events, actions, etc. is an