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

TL;DR

This paper introduces a rapid partial video copy detection method leveraging KNN search on global features, significantly improving accuracy and efficiency over existing techniques.

Contribution

It presents a novel framework combining KNN search with a modified temporal network for fast and accurate partial video copy detection.

Findings

F1 score surpasses state-of-the-art methods

Efficient retrieval with KNN search reduces processing time

Effective localization of copied segments in videos

Abstract

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.