VIDEO SHOT BOUNDARY DETECTION(15)

BOUNDARY DETECTION(15)

SVM is used in this method for shot boundary detection. Support Vector Machines (SVM) is a statistical learning method based on the structure risk minimization principle. It has been very efficiently proved to be useful in many pattern recognition applications. In the case of binary classification, the objective of the SVM is to find the best separating hyperplane with a maximum margin. To train this classifier, we manually annotated frames in the training data. Using the trained classifier, we can label a sequence of frames.

Since the classifier occasionally produces false predictions due to the variations caused by photo flashes, rapid camera movement, and object movement, only using the perfect match between the predefined patterns and sub-sequences usually skips many of the true shot boundaries. Instead, we use a more flexible matching algorithm in which a match is declared if a Since the classifier occasionally produces false predictions due to the variations caused by photo flashes, rapid camera movement, and object movement, only using the perfect match between the predefined patterns and sub-sequences usually skips many of the true shot boundaries. Instead, we use a more flexible matching algorithm in which a match is declared if a portion of the predefined pattern is found in the input subsequence.

Representative APR 391%

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