Content-Based Video Copy Detection Benchmarking at TRECVID

Author:

Awad George1,Over Paul2,Kraaij Wessel3

Affiliation:

1. Dakota Consulting, Inc., Silver Spring, MD

2. National Institute of Standards and Technology, Gaithersburg, MD

3. TNO, Radboud University, Netherlands

Abstract

This article presents an overview of the video copy detection benchmark which was run over a period of 4 years (2008--2011) as part of the TREC Video Retrieval (TRECVID) workshop series. The main contributions of the article include i) an examination of the evolving design of the evaluation framework and its components (system tasks, data, measures); ii) a high-level overview of results and best-performing approaches; and iii) a discussion of lessons learned over the four years. The content-based copy detection (CCD) benchmark worked with a large collection of synthetic queries, which is atypical for TRECVID, as was the use of a normalized detection cost framework. These particular evaluation design choices are motivated and appraised.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

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