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Salient features for visual word based image copy detection

Authors:

Michael S. LewAuthors Info & Claims

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

Pages 475 - 478

https://doi.org/10.1145/2578726.2578797

Published: 01 April 2014 Publication History

Abstract

Image copy detection is a major challenge with regard to computational efficiency, memory requirements and accuracy. A popular approach from the literature is to use visual words (or Bag-of-Words) constructed from real value (SIFT and SURF) and binary string salient point descriptors (BRIEF, ORB, BRISK and FREAK). To accommodate large scale data sets, we used the approximate nearest neighbor (ANN) based cluster approach. Our results on several well-known test sets reveal that some of the recent binary string approaches outperformed notable descriptors such as SIFT and SURF.

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Cited By

Zhou ZWang YWu QYang CSun X(2017)Effective and Efficient Global Context Verification for Image Copy DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2016.260106512:1(48-63)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TIFS.2016.2601065
Wu SOerlemans ABakker ELew M(2017)A comprehensive evaluation of local detectors and descriptorsSignal Processing: Image Communication10.1016/j.image.2017.06.01059(150-167)Online publication date: Nov-2017
https://doi.org/10.1016/j.image.2017.06.010

Index Terms

Salient features for visual word based image copy detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Multimedia information systems

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Published In

cover image ACM Other conferences

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

April 2014

564 pages

ISBN:9781450327824

DOI:10.1145/2578726

Conference Chairs:
Mohan Kankanhalli
National University of Singapore
,
Stefan Rueger
The Open University, UK
,
R. Manmatha
A9.com, USA
,
General Chairs:
Joemon Jose
University of Glasgow, UK
,
Keith van Rijsbergen
University of Glasgow, UK

Copyright © 2014 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2014

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ICMR '14

ICMR '14: International Conference on Multimedia Retrieval

April 1 - 4, 2014

Glasgow, United Kingdom

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ICMR '14 Paper Acceptance Rate 21 of 111 submissions, 19%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Zhou ZWang YWu QYang CSun X(2017)Effective and Efficient Global Context Verification for Image Copy DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2016.260106512:1(48-63)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TIFS.2016.2601065
Wu SOerlemans ABakker ELew M(2017)A comprehensive evaluation of local detectors and descriptorsSignal Processing: Image Communication10.1016/j.image.2017.06.01059(150-167)Online publication date: Nov-2017
https://doi.org/10.1016/j.image.2017.06.010

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