A Fast Partial Video Copy Detection Using KNN and Global Feature Database

05/04/2021
by   Weijun Tan, et al.
0

We propose a fast partial video copy detection framework in this paper. In this framework all frame features of the reference videos are organized in a KNN searchable database. Instead of scanning all reference videos, the query video segment does a fast KNN search in the global feature database. The returned results are used to generate a short list of candidate videos. A modified temporal network is then used to localize the copy segment in the candidate videos. We evaluate different choice of CNN features on the VCDB dataset. Our benchmark F1 score exceeds the state of the art by a big margin.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/20/2019

Video Segment Copy Detection Using Memory Constrained Hierarchical Batch-Normalized LSTM Autoencoder

In this report, we introduce a video hashing method for scalable video s...
research
10/28/2016

Recent advances in content based video copy detection

With the immense number of videos being uploaded to the video sharing si...
research
08/04/2021

Video Similarity and Alignment Learning on Partial Video Copy Detection

Existing video copy detection methods generally measure video similarity...
research
08/05/2018

Video Re-localization

Many methods have been developed to help people find the video contents ...
research
05/24/2022

A Benchmark and Asymmetrical-Similarity Learning for Practical Image Copy Detection

Image copy detection (ICD) aims to determine whether a query image is an...
research
06/15/2023

The 2023 Video Similarity Dataset and Challenge

This work introduces a dataset, benchmark, and challenge for the problem...
research
03/14/2017

A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

We propose a new algorithm for the reliable detection and localization o...

Please sign up or login with your details

Forgot password? Click here to reset