Video Copy Detection Based on Multiple Visual Feature Matching

Video copy detection is an important technique for filtering out unnecessary video content which is copyright invaded or redundant for large video databases.The difficulties of detecting video copies lie in the dissimilarities between video and its copies.Various visual transformations make the existing methods invalid where a single feature is used for video matching.This paper proposes a novel method which uses multiple visual features for content matching and searching.The proposed method adopts the cascade detection approach which first retrieves the query video using global features,then videos is matched by more accurate local features.To achieve satisfactory efficiency for large scale applications,the kd-tree indexing structure is adopted.Experiments on benchmark dataset show that the proposed method manifests a good balance of robustness to common copy transformations and high detection efficiency.