An improved logo detection method with learning-based verification for video classification

Hyo Young Kim; Mun Cheon Kang; Sung Ho Chae; Dae Hwan Kim; Sung Jea Ko

doi:10.1109/ICCE-Berlin.2014.7034299

An improved logo detection method with learning-based verification for video classification

Hyo Young Kim
, Mun Cheon Kang
, Sung Ho Chae
, Dae Hwan Kim
, Sung Jea Ko

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

With the growth of cloud services, concerns have been raised regarding illegal sharing of the commercial video. To prevent the illegal sharing automatically, the method for classifying video as 'commercial' or 'noncommercial' is essentially required. Since most commercial video has a logo as a visible watermark, automatic logo detection can be an efficient method for the video classification. In this paper, we present an improved logo detection method which correctly detects the logo in any types of video using learning-based logo verification. Experimental results show that the proposed method achieves improved detection performance as compared with the existing method, and thus can be effectively used for classifying the video.

Original language	English
Title of host publication	Proceedings 2014 IEEE 4th International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Editors	Francisco J. Bellido, Dietmar Hepper, Hans L. Cycon, Alexander Huhn
Publisher	IEEE Computer Society
Pages	192-193
Number of pages	2
Edition	February
ISBN (Electronic)	9781479961658
DOIs	https://doi.org/10.1109/ICCE-Berlin.2014.7034299
Publication status	Published - 2015 Feb 5
Event	2014 4th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin - Berlin, Germany Duration: 2014 Sept 7 → 2014 Sept 10

Publication series

Name	IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Number	February
Volume	2015-February
ISSN (Print)	2166-6814
ISSN (Electronic)	2166-6822

Other

Other	2014 4th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Country/Territory	Germany
City	Berlin
Period	14/9/7 → 14/9/10

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

SVM
Video classification
copyright protection
feature extraction
logo detection

ASJC Scopus subject areas

Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Media Technology

Access to Document

10.1109/ICCE-Berlin.2014.7034299

Cite this

Kim, H. Y., Kang, M. C., Chae, S. H., Kim, D. H., & Ko, S. J. (2015). An improved logo detection method with learning-based verification for video classification. In F. J. Bellido, D. Hepper, H. L. Cycon, & A. Huhn (Eds.), Proceedings 2014 IEEE 4th International Conference on Consumer Electronics - Berlin, ICCE-Berlin (February ed., pp. 192-193). Article 7034299 (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin; Vol. 2015-February, No. February). IEEE Computer Society. https://doi.org/10.1109/ICCE-Berlin.2014.7034299

An improved logo detection method with learning-based verification for video classification. / Kim, Hyo Young; Kang, Mun Cheon; Chae, Sung Ho et al.
Proceedings 2014 IEEE 4th International Conference on Consumer Electronics - Berlin, ICCE-Berlin. ed. / Francisco J. Bellido; Dietmar Hepper; Hans L. Cycon; Alexander Huhn. February. ed. IEEE Computer Society, 2015. p. 192-193 7034299 (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin; Vol. 2015-February, No. February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, HY, Kang, MC, Chae, SH, Kim, DH & Ko, SJ 2015, An improved logo detection method with learning-based verification for video classification. in FJ Bellido, D Hepper, HL Cycon & A Huhn (eds), Proceedings 2014 IEEE 4th International Conference on Consumer Electronics - Berlin, ICCE-Berlin. February edn, 7034299, IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin, no. February, vol. 2015-February, IEEE Computer Society, pp. 192-193, 2014 4th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin, Berlin, Germany, 14/9/7. https://doi.org/10.1109/ICCE-Berlin.2014.7034299

Kim HY, Kang MC, Chae SH, Kim DH, Ko SJ. An improved logo detection method with learning-based verification for video classification. In Bellido FJ, Hepper D, Cycon HL, Huhn A, editors, Proceedings 2014 IEEE 4th International Conference on Consumer Electronics - Berlin, ICCE-Berlin. February ed. IEEE Computer Society. 2015. p. 192-193. 7034299. (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin; February). doi: 10.1109/ICCE-Berlin.2014.7034299

Kim, Hyo Young ; Kang, Mun Cheon ; Chae, Sung Ho et al. / An improved logo detection method with learning-based verification for video classification. Proceedings 2014 IEEE 4th International Conference on Consumer Electronics - Berlin, ICCE-Berlin. editor / Francisco J. Bellido ; Dietmar Hepper ; Hans L. Cycon ; Alexander Huhn. February. ed. IEEE Computer Society, 2015. pp. 192-193 (IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin; February).

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abstract = "With the growth of cloud services, concerns have been raised regarding illegal sharing of the commercial video. To prevent the illegal sharing automatically, the method for classifying video as 'commercial' or 'noncommercial' is essentially required. Since most commercial video has a logo as a visible watermark, automatic logo detection can be an efficient method for the video classification. In this paper, we present an improved logo detection method which correctly detects the logo in any types of video using learning-based logo verification. Experimental results show that the proposed method achieves improved detection performance as compared with the existing method, and thus can be effectively used for classifying the video.",

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AB - With the growth of cloud services, concerns have been raised regarding illegal sharing of the commercial video. To prevent the illegal sharing automatically, the method for classifying video as 'commercial' or 'noncommercial' is essentially required. Since most commercial video has a logo as a visible watermark, automatic logo detection can be an efficient method for the video classification. In this paper, we present an improved logo detection method which correctly detects the logo in any types of video using learning-based logo verification. Experimental results show that the proposed method achieves improved detection performance as compared with the existing method, and thus can be effectively used for classifying the video.

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An improved logo detection method with learning-based verification for video classification

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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