  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.
