Research On Gradient Detection Of Video Abstract Algorithm Based On Feature Fusion

With the rapid development of information technology and computer science,video data plays an increasingly significant role in daily life.In diverse video data,how to fast scan and detect contents satisfied the user's requirement brings great challenging to video abstract technology.Therefore,research on video abstract has been a hot research topic.In existing video abstract techniques,shot boundary detection and key frame extraction are two important branches.This thesis proposes relevant modified strategies for the shortages of algorithm.The following summarizes the main contents of this dissertation.1.Firstly,this study mainly reviews the relevant research background,study status and existing problems of video abstract technology,and presents the global and local characteristics of image.Besides that,this dissertation also provides recent research progress of shot boundary detection and key frame extraction and its application in relevant areas.2.Secondly,in the shot boundary detection,the motion information often causes serious interference,and affects the accuracy of image detection.For the existing problems about shot detection,this paper proposes optical flow-based shot detection algorithm.For the video motion information obtained by optical flow method,a quantitative method is proposed to improve the accuracy of the continuous detection,to gain candidate shot boundary.It is difficult to identify the type of abrupt and gradual shot depended on single pattern.Hence,this dissertation employs multi-feature approach for detecting gradually.Simulation experiments are carried on some widely employed video libraries for assessing the performance of algorithms.Results demonstrate that the proposed methodology improves the Precision and Recall rate effectively.3.Finally,in the key frame extraction algorithms,some clustering methods need priori class number,which increases the limitation of approaches.Meanwhile,most of the key frame extraction algorithms have high complexity for mass video.In this paper,a new video key frame selection algorithm is proposed.Video frames are mapped to the corresponding points in two-dimensional space on the similarity relations.Finding category centers using clustering algorithm,and selecting the key frame set from the original video on the similarity relations.Simulation experiments are executed on video data sets,results indicate that the proposed strategy improves the Precision and Recall rate effectively.