Research Of Video Content Detection Based On Local Visual Feature

With the development and popularization of digital media technology and networktechnology, digital video production, transmission and application become more andmore simple. The rapid growth of digital video has provided convenience for people.However, the massive digital video on the Web also brought a variety of issues, such asthe release of illegal, obscene and pirated content through the network. How tosupervise the content of a large amount of digital videos effectively becomes an urgentproblem to be solved.Local visual features can outperform global features on visual content detectionand object recognition. This paper presents a new method for video content detection byusing local visual features. An image can be represented by a set of local features byextracting several local features. The set of local features is always high-dimensionaland large amount, which results in the large storage space and computing time. Anindependent summarized vector is used to describe the set of local features in this paper,which can largely reduce the storage space and computation complexity by using thefilter-and refine strategy. Based on summarized vector, a new hierarchical indexingframework is established. First, a set of local features is embedded into a singlesummarized vector, which is a high-dimensional histogram. A two-stage search schemeis performed by utilizing summarized vector for fast filtering in the first stage. Only asmall set of candidates is accessed for further investigation by point-to-point matchingin the second stage. We have designed and implemented an on-line video copy detectionsystem for demonstration. Our extensive experiments on a large database of more than10,000video clips demonstrate that the video copy detection system can real-timeretrieval the copy video clips with high accuracy.