Simple Yet Efficient Content Based Video Copy Detection
This addresses the challenge of multimedia retrieval for video copy detection, offering improvements in efficiency and accuracy, though it appears incremental as it builds on existing methods.
The paper tackled the problem of efficiently detecting content-based copies in large video collections with high accuracy, proposing a new algorithm that achieved scores of 100% and at least 93% on the MuscleVCD ST1 benchmark dataset.
Given a collection of videos, how to detect content-based copies efficiently with high accuracy? Detecting copies in large video collections still remains one of the major challenges of multimedia retrieval. While many video copy detection approaches show high computation times and insufficient quality, we propose a new efficient content-based video copy detection algorithm improving both aspects. The idea of our approach consists in utilizing self-similarity matrices as video descriptors in order to capture different visual properties. We benchmark our algorithm on the MuscleVCD ST1 benchmark dataset and show that our approach is able to achieve a score of 100\% and a score of at least 93\% in a wide range of parameters.