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Shot boundary detection implies the automated detection of transitions between shots.
Given its importance, shot boundary detection is still a much studied topic [18 20].
Shot boundary detection is used to segment videos into different shots.
We study the problem of video shot boundary detection using an adaptive edge-oriented framework.
This article explores the problem of video shot boundary detection and examines a novel shot boundary detection algorithm by using QR-decomposition and modeling of gradual transitions by Gaussian functions.
Video shot boundaries can be classified into two types: abrupt shot boundary (ASB) (as in Fig. 2) and gradual shot boundary (GSB), according to a certain classification of scene transition, which in general is related to content variation over time.
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There have been a huge number of methods for the summarization of well-edited videos such as movies and TV contents [3 6], where a well-edited video means that it has clear shot boundaries and was shot under well-controlled environments with stable and high-quality cameras.
Both of these types of shot boundaries can come in many different forms.
This similarity of visual effects caused by camera operations and object motion can induce false detections of gradual shot boundaries.
Ferreira et al. [11] proposed an algorithm to detect 3D shot boundaries (3DSB) based on a joint depth-temporal criterion.
These videos have many unclear shot boundaries, and they also have meaningless, redundant, or low-quality frames.
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