Association Rule Mining for Identifying Worst and Best Drugs Information

Abstract

We propose a split-based and graph-based video sequence matching method for video copy detection. In particular, SIFT descriptors are used to describe video content due to the excellent stability and discrimination of local features. However, SIFT descriptor-based matching is computationally expensive for large numbers of points and high dimensions. So, to reduce computational complexity, these large numbers of videos have a large number of copies or almost duplicated videos. According to average statistics, 27% of duplicate videos in search results on Google Video, YouTube and Yahoo! duplicate or nearly duplicate the most popular version of the video. Video search engine. As a result, effective methods for video copy detection are becoming increasingly important. An effective video copy detection method is based on the fact that "the video itself is a watermark" and makes full use of the video content to detect the copy. It can also reduce noise caused by matching spatial characteristics. And it adapts to changes in the video frame rate. The experimental results also prove the effectiveness of the method.