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.




Here we are using four different methods involved in video shot boundary detection for comparison, GIST, Color Histogram, Segmentation and Motion Activity Descriptor.

Color Histogram Method

This technique is able to differentiate abrupt shot boundaries by the analysis of color histogram differences and smooth shot boundaries but temporal color variation. This provides a simple and fast algorithm able to work in real-time with reasonable high performances in a video indexing tool.



Video Shot Boundary Detection Using Gist

Gist has been shown to characterize the structure of images well while being resistant to luminance change and also small translation. We use gist representation to model the global appearance of the scene. Gist treats the scene as one object, which can be characterized by consistent global and local structure. This method differs from prior work that characterizes scene by the identity of objects present in the image. Gist has been shown to perform well on scene category recognition. Gist also provides good contextual prior for facilitating object recognition task. Within one shot, due to the motion, appearance or disappearance of objects, the color histogram may not be consistent. Gist captures the overall texture of the background while ignoring these small change due to foreground objects.




Video Shot Boundary Detection Using QR-Decomposition and Gaussian Transition Detection

The algorithm utilizes the properties of QR-decomposition and extracts a block-wise probability function that illustrates the probability of video frames to be in shot transitions. The probability function has abrupt changes in hard cut transitions, and semi-Gaussian behaviour in gradual transitions. The algorithm detects these transitions by analyzing the probability function.



In this work, we used the extraction of key frames method based on detecting a significant change in the activity of motion. To jump 2 images which do not distort the calculations but we can minimize the execution time. First we extract the motion vectors between image i and image i+2 then calculates the intensity of motion, we repeat this process until reaching the last frame of the video and comparing the difference between the intensities of successive motion to a specified threshold.




The text segmentation based approaches in natural language processing can be used. The shot boundary detection process for a given video is carried out through two main stages. In the first stage, frames are extracted and labelled with pre-defined labels. In the second stage, the shot boundaries are identified by grouping the labelled frames into segments. We use the following six labels to label frames in a video: 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 (post-frame of a GRADUAL transition).

Given a sequence of labelled frames, the shot boundaries and transition types are identified by looking up and processing the frames marked with a non NORM FRM label.




Video shot boundary detection using Macroblocks

The third of our methods for shot boundary detection, unlike the others, is based on processing the encoded version of the video. One of the features of MPEG-1 encoding is that each frame is broken into a fixed number of segments called macroblocks and there are three types of these macroblocks, I-, P- and B-. The classification of the different macroblock types is done at the encoder, based on the motion estimation and efficiency of the encoding.


Representative APR 391%

Let's say you want to borrow $100 for two week. Lender can charge you $15 for borrowing $100 for two weeks. You will need to return $115 to the lender at the end of 2 weeks. The cost of the $100 loan is a $15 finance charge and an annual percentage rate of 391 percent. If you decide to roll over the loan for another two weeks, lender can charge you another $15. If you roll-over the loan three times, the finance charge would climb to $60 to borrow the $100.

Implications of Non-payment: Some lenders in our network may automatically roll over your existing loan for another two weeks if you don't pay back the loan on time. Fees for renewing the loan range from lender to lender. Most of the time these fees equal the fees you paid to get the initial payday loan. We ask lenders in our network to follow legal and ethical collection practices set by industry associations and government agencies. Non-payment of a payday loan might negatively effect your credit history.

Calculate APR