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Automatic logo transition detection in digital video contents

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dc.contributor.authorLu, Chang-Yu-
dc.contributor.authorRoh, Myung-Cheol-
dc.contributor.authorKang, Seung-Yeon-
dc.contributor.authorLee, Seong-Whan-
dc.date.accessioned2021-09-06T20:25:16Z-
dc.date.available2021-09-06T20:25:16Z-
dc.date.created2021-06-18-
dc.date.issued2012-05-
dc.identifier.issn1433-7541-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/108560-
dc.description.abstractThe amount of user created contents has been increasing rapidly and is associated with a serious copyright problem. Automatic logo detection and recognition in videos is a natural and efficient way of overcoming the copyright problem. However, logos have varying characteristics, which make logo detection and recognition very difficult. Moreover, logo transitions between two different logos exist in one video comprising several video contents. This disrupts the automatic logo detection and recognition. Therefore, in order to improve logo detection, it is necessary to take into account the logo transitions explicitly. This paper proposes an accurate logo transition detection method for recognizing logos in digital video contents. The proposed method accurately segments a video according to logo and efficiently recognizes various types of logos. The experimental results demonstrate the effectiveness of the proposed method for logo detection and video segmentation according to logo.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherSPRINGER-
dc.subjectEXTRACTION-
dc.subjectTEXT-
dc.titleAutomatic logo transition detection in digital video contents-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, Seong-Whan-
dc.identifier.doi10.1007/s10044-012-0267-9-
dc.identifier.scopusid2-s2.0-84862785567-
dc.identifier.wosid000303384000007-
dc.identifier.bibliographicCitationPATTERN ANALYSIS AND APPLICATIONS, v.15, no.2, pp.175 - 187-
dc.relation.isPartOfPATTERN ANALYSIS AND APPLICATIONS-
dc.citation.titlePATTERN ANALYSIS AND APPLICATIONS-
dc.citation.volume15-
dc.citation.number2-
dc.citation.startPage175-
dc.citation.endPage187-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.subject.keywordPlusEXTRACTION-
dc.subject.keywordPlusTEXT-
dc.subject.keywordAuthorLogo transition detection-
dc.subject.keywordAuthorLogo detection and recognition-
dc.subject.keywordAuthorSub-video merging-
dc.subject.keywordAuthorVideo segmentation based on logos-
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