V ideo copy-detection, the purpose of which is to find a video copy in a repository, is important for many applications. 1-3 Our research focuses on locating video clips that are copied but maintain frame correspondence. Although this issue has been investigated for several years in multimedia research, efficient and scalable copy-detection is still a challenging problem due to the considerable computational costs. Copy location can be seen as an extension of copy detection. Rather than merely detecting the existence of a query video in a database, the location system must find the query in the database, a process that introduces additional computational costs. Despite the computational costs, copy location is valuable for many applications. For example, if we would like to see whether a video program, such as an advertisement, is broadcast in a large video program repository, copy location can be used to find the location of the specified program. Also, it can be used in many other applications, including video data mining, copyright protection, and so on. This article proposes a fast video copylocation method that can find the location of a given query in a large video repository in real time, regardless of the query length. The method proposed here consists of two major contributions. First, it includes a probabilistic model for video copy location to formulate the task as a likelihood maximization problem. Second, it includes a simplified approach to reduce the maximization problem into a set of 0-1 value problems based on the indexing structure. For copy location, it's necessary to project the video sequence into a certain feature space and transform video copy location into a feature-matching problem in this feature space. For each video V, it can be first segmented into a set of frames V ¼ {v i , i ¼ 1, 2, . . . , n}, where v i is the i-th constitutive frame in V. Given a certain feature descriptor D, videos can be mapped into a set of feature vectors, that is,
Liu et al. REF performed video copy location detection, where the goal is to find copies of a clip within a video repository with certain transformations applied to it, such as blurring.
Real-Time Video Copy-Location Detection in Large-Scale Repositories
IEEE MultiMedia
IEEE MultiMedia
Computer Science