Partial Copy Detection in Videos: A Benchmark and an Evaluation of Popular Methods
Yu–Gang Jiang, Jiajun Wang · IEEE Transactions on Big Data · 2016
The goal of partial video copy detection is to find one or more segments of a query video which have (transformed) copies in a large dataset. Previous related research in this field used either small-scale datasets or large datasets with simulated partial copies by imposing several pre-defined transformations (e.g., photometric changes) due to the extremely time-consuming annotation of real copies. It is still unknown how well the techniques developed on simulated datasets perform on real copies, which are much more challenging and too complex to be simulated. In this paper, we introduce a large-scale video copy database (VCDB) with over 100,000 videos, and more than 9,000 copy pairs found by manual annotation. A state-of-the-art system of video copy detection is evaluated on VCDB to show the limitations of existing techniques. We also evaluate deep learning features learned by two neural networks: one is independently trained on a different dataset and the other is tailored to deal with the copy detection task. Our evaluation suggests that all the existing techniques, including the deep learning features, are far from satisfactory in detecting complex real copies.