scholarly journals Video Searching and Fingerprint Detection by Using the Image Query and PlaceNet-Based Shot Boundary Detection Method

2018 ◽  
Vol 8 (10) ◽  
pp. 1735 ◽  
Author(s):  
DaYou Jiang ◽  
Jongweon Kim
Keyword(s):  
Boundary Detection ◽  
Shot Boundary Detection ◽  
Good Precision ◽  
Scale Invariant ◽  
Query Image ◽  
Shot Boundary ◽  
Video Searching ◽  
Image Query ◽  
Value Decomposition ◽  
Fingerprint Detection

This work presents a novel shot boundary detection (SBD) method based on the Place-centric deep network (PlaceNet), with the aim of using video shots and image queries for video searching (VS) and fingerprint detection. The SBD method has three stages. In the first stage, we employed Local Binary Pattern-Singular Value Decomposition (LBP-SVD) features for candidate shot boundaries selection. In the second stage, we used the PlaceNet to select the shot boundary by semantic labels. In the third stage, we used the Scale-Invariant Feature Transform (SIFT) descriptor to eliminate falsely detected boundaries. The experimental results show that our SBD method is effective on a series of SBD datasets. In addition, video searching experiments are conducted by using one query image instead of video sequences. The results under several image transitions by using shot fingerprints have shown good precision.

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>

An Efficient Shot Boundary Detection Using Data-cube Searching Technique

2020 ◽  
Vol 13 (4) ◽  
pp. 798-807
Author(s):  
J. Kavitha ◽  
P. Arockia Jansi Rani ◽  
P. Mohamed Fathimal ◽  
Asha Paul
Keyword(s):  
Texture Features ◽  
Boundary Detection ◽  
Data Cube ◽  
Multimedia Data ◽  
Shot Boundary Detection ◽  
Gradual Transition ◽  
Detection Rates ◽  
Effective Manner ◽  
Shot Boundary ◽  
Shot Transitions

Background:: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective:: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results:: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion:: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.

Sign in / Sign up

Export Citation Format

Share Document