Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simple Yet Efficient Content Based Video Copy Detection   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.

9
The categories of this paper are: nlin
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph