Article

Trademark matching and retrieval in sports video databases

Authors:

Andrew D. Bagdanov,

Lamberto Ballan,

Alberto Del BimboAuthors Info & Claims

MIR '07: Proceedings of the international workshop on Workshop on multimedia information retrieval

Pages 79 - 86

https://doi.org/10.1145/1290082.1290096

Published: 24 September 2007 Publication History

Abstract

In this paper we describe a system for detection and retrieval of trademarks appearing in sports videos. We propose a compact representation of trademarks and video frame content based on SIFT feature points. This representation can be used to robustly detect, localize, and retrieve trademarks as they appear in a variety of different sports video types. Classification of trademarks is performed by matching a set of SIFT feature descriptors for each trademark instance against the set of SIFT features detected in each frame of the video. Localization is performed through robust clustering of matched feature points in the video frame. Experimental results are provided, along with an analysis of the precision and recall. Results show that the our proposed technique is efficient and effectively detects and classifies trademarks.

References

[1]

F. Aldershoff and T. Gevers. Visual tracking and localisation of billboards in streamed soccer matches. In SPIE Electronic Imaging, San Jose, CA, USA, 2004.

[2]

S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4):509--522, 2002.

Digital Library

[3]

C. Chmid and R. Mohr. Local Grayvalue Invariants for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5):530--535, 1997.

Digital Library

[4]

R. J. M. Den Hollander and A. Hanjalic. Logo detection in video by string matching. In Proceedings of IEEE International Conference on Image Processing (ICIP), Barcelona, ES, 2003.

[5]

R. J. M. Den Hollander and A. Hanjalic. Logo recognition in video by line profile classification. In SPIE Electronic Imaging, San Jose, CA, USA, 2004.

[6]

J. P. Eakins, K. J. Riley, and J. D. Edwards. Shape Feature Matching for Trademark Image Retrieval. In Lecture Notes in Computer Science, volume 2728, pages 439--443, 2003.

Digital Library

[7]

T. Gevers and A. W. M. Smeulders. Color-based object recognition. Pattern Recognition, 32:453--464, 1999.

[8]

G. Kienast, H. Stiegler, W. Bailer, H. Rehatschek, S. Busemann, and T. Declerck. Sponsorship tracking using distributed multi-modal analysis (DIRECT-INFO). In The 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology, 2005. EWIMT 2005., pages 341--348, 2005.

[9]

Y. S. Kim and W. Y. Kim. Content-based trademark retrieval system using visually salient features. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 307--312, 1997.

Digital Library

[10]

B. Kovar and A. Hanjalic. Logo appearance statistics in a sport video: Video indexing for sponsorship revenue control. In SPIE Electronic Imaging 2002: Storage and Retrieval for Media Databases, San Jose, CA, USA, 2002.

[11]

Y. Lamdan, J. T. Schwartz, and H. J. Wolfson. Object recognition by affine invariant matching. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 335--344, Ann Arbor, MI, USA, 1988.

[12]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--1, 2004.

Digital Library

[13]

F. Pelisson, D. Hall, O. Riff, and J. L. Crowley. Brand identification using gaussian derivative histograms. In Lecture Notes in Computer Science, volume 2623, pages 492--501, 2003.

Digital Library

[14]

R. H. Van Leuken, M. F. Demirci, V. J. Hodge, J. Austin, and R. C. Veltkamp. Layout indexing of trademark images. In Proceedings of the 6th International Conference on Image and Video Retrieval (CIVR), Amsterdam, NL, 2007.

Digital Library

[15]

P. Y. Yin and C. C. Yeh. Content-based retrieval from trademark databases. Pattern Recognition Letters, 23(1-3):113--126, 2002.

Digital Library

Cited By

Yin CYe QZhang SYang Z(2024)Detecting Logos for Indoor Environmental Perception Using Unsupervised and Few-Shot LearningElectronics10.3390/electronics1312224613:12(2246)Online publication date: 7-Jun-2024
https://doi.org/10.3390/electronics13122246
Chen XBi HHu C(2023)Trademark Detection Based on Improved SSD AlgorithmProceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing10.1145/3639479.3639483(14-20)Online publication date: 27-Dec-2023
https://dl.acm.org/doi/10.1145/3639479.3639483
Song JKurniawati H(2019)Exploiting Trademark Databases for Robotic Object Fetching2019 International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2019.8793829(4946-4952)Online publication date: May-2019
https://doi.org/10.1109/ICRA.2019.8793829
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Trademark matching and retrieval in sports video databases

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Published In

cover image ACM Conferences

MIR '07: Proceedings of the international workshop on Workshop on multimedia information retrieval

September 2007

343 pages

ISBN:9781595937780

DOI:10.1145/1290082

General Chairs:
James Z. Wang
The Pennsylvania State University, USA
,
Nozha Boujemaa
INRIA Rocquencourt, France
,
Program Chairs:
Alberto Del Bimbo
University of Florence, Italy
,
Jia Li
The Pennsylvania State University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 September 2007

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MM07

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MM07: The 15th ACM International Conference on Multimedia 2007

September 24 - 29, 2007

Bavaria, Augsburg, Germany

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Cited By

Yin CYe QZhang SYang Z(2024)Detecting Logos for Indoor Environmental Perception Using Unsupervised and Few-Shot LearningElectronics10.3390/electronics1312224613:12(2246)Online publication date: 7-Jun-2024
https://doi.org/10.3390/electronics13122246
Chen XBi HHu C(2023)Trademark Detection Based on Improved SSD AlgorithmProceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing10.1145/3639479.3639483(14-20)Online publication date: 27-Dec-2023
https://dl.acm.org/doi/10.1145/3639479.3639483
Song JKurniawati H(2019)Exploiting Trademark Databases for Robotic Object Fetching2019 International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2019.8793829(4946-4952)Online publication date: May-2019
https://doi.org/10.1109/ICRA.2019.8793829
Stolikj MJarnikov DWajs A(2018)Artificial Intelligence for Detecting Media PiracySMPTE Motion Imaging Journal10.5594/JMI.2018.2827181127:6(22-27)Online publication date: Jul-2018
https://doi.org/10.5594/JMI.2018.2827181
Das T(2018)Logo Matching and Recognition Based on ContextFeature Dimension Reduction for Content-Based Image Identification10.4018/978-1-5225-5775-3.ch009(164-176)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5775-3.ch009
Hou SZhou SLiu WZheng Y(2018)Classifying advertising video by topicalizing high-level semantic conceptsMultimedia Tools and Applications10.1007/s11042-018-5801-377:19(25475-25511)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11042-018-5801-3
Wang MMing YLiu QYin J(2017)Image-Based Video Retrieval Using Deep Feature2017 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP.2017.7947017(1-6)Online publication date: May-2017
https://doi.org/10.1109/SMARTCOMP.2017.7947017
Wang YNg V(2017)Anomaly Detection with Attribute Conflict Identification in Bank Customer Data2017 IEEE International Conference on Smart Computing (SMARTCOMP)10.1109/SMARTCOMP.2017.7946988(1-6)Online publication date: May-2017
https://doi.org/10.1109/SMARTCOMP.2017.7946988
Bombonato LCamara-Chavez GSilva P(2017)Real-Time Single-Shot Brand Logo Recognition2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI.2017.24(134-140)Online publication date: Oct-2017
https://doi.org/10.1109/SIBGRAPI.2017.24
Kondekar PShende P(2016)Logo recognition using Context Dependent criteria2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)10.1109/ICAECCT.2016.7942565(111-115)Online publication date: Dec-2016
https://doi.org/10.1109/ICAECCT.2016.7942565
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