Shot boundary detection using scale invariant feature matching

2006 ◽  
Author(s):  
Min-Ho Park ◽  
Rae-Hong Park ◽  
Sang Wook Lee
Keyword(s):  
Feature Matching ◽  
Boundary Detection ◽  
Shot Boundary Detection ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Shot Boundary ◽  
Scale Invariant Feature

Video Shot Boundary Detection in Sport Video Using the Scale Invariant Feature Transform

2016 ◽  
Vol 850 ◽  
pp. 152-158
Author(s):  
Zaynab El Khattabi ◽  
Youness Tabii ◽  
Abdelhamid Benkaddour
Keyword(s):  
Boundary Detection ◽  
Scale Invariant Feature Transform ◽  
Shot Boundary Detection ◽  
Scale Invariant ◽  
Temporal Sampling ◽  
Invariant Feature ◽  
Sport Video ◽  
Shot Boundary ◽  
Feature Transform ◽  
Scale Invariant Feature

The main purpose of shot boundary detection is to detect visual content changes between consecutives frames of a video. In this paper, a new shot boundary detection algorithm is proposed based on the scale invariant feature transform (SIFT). The first stage consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. A temporal sampling period is used to avoid the frame by frame processing. The overview step provides the changes of matched features ratio all along the video. Secondly, a function is performed to detect the shot boundaries. The proposed method can be used for detecting gradual transitions as well as hard cuts and without requiring any training of the video content in advance. Experiments have been conducted on sports video and show that this algorithm achieves good results in detecting both abrupt and gradual transitions.

scholarly journals Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels

2017 ◽  
Vol 7 (5) ◽  
pp. 2565 ◽  
Author(s):  
Zaynab El khattabi ◽  
Youness Tabii ◽  
Abdelhamid Benkaddour
Keyword(s):  
Moving Average ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Scale Invariant Feature Transform ◽  
Shot Boundary Detection ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Shot Boundary ◽  
Feature Transform ◽  
Scale Invariant Feature

<p>Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.</p>

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

An Improved SIFT Matching Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 1232-1237 ◽  
Author(s):  
Can Ding ◽  
Chang Wen Qu ◽  
Feng Su
Keyword(s):  
High Dimension ◽  
Gradient Method ◽  
Feature Matching ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
New Feature ◽  
Scale Invariant Feature

The high dimension and complexity of feature descriptor of Scale Invariant Feature Transform (SIFT), not only occupy the memory spaces, but also influence the speed of feature matching. We adopt the statistic feature point’s neighbor gradient method, the local statistic area is constructed by 8 concentric square ring feature of points-centered, compute gradient of these pixels, and statistic gradient accumulated value of 8 directions, and then descending sort them, at last normalize them. The new feature descriptor descend dimension of feature from 128 to 64, the proposed method can improve matching speed and keep matching precision at the same time.

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