Video Copy Detection Based on Path Merging and Query Content Prediction

Content-based video copy detection is undoubtedly one of the most effective solutions to video content tracing and copyright protection. It extracts features from videos and determines whether a copy occurs by comparing the extracted features. While a lot of work has been reported to address this problem with good performance, very few considered it from the perspective of a dynamic searching process. In this paper, we treat the copy detection in video streams as a sequential matching problem and take into consideration the connections between temporary results and forthcoming input. Specifically, we propose a video copy detection system that involves a novel frame fusion scheme and an adaptive search strategy. The proposed frame fusion scheme relies on path merging in a graph model, which is able to work in an online manner and provide informative temporary fusion results. Based on these temporary results, query content predictions can be generated, which will be fed back to the frame search engine to instruct it to adaptively adjust the search strategy. The experimental results show that the proposed frame fusion scheme achieves competitive detection and localization accuracies compared with the state-of-the-art methods. Meanwhile, with the assistance of the adaptive search strategy, the computational complexity of frame similarity search is dramatically reduced at a cost of a slight decrease in accuracy.