2009
DOI: 10.1007/978-3-642-01513-7_65
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Video Fingerprinting by Using Boosted Features

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Cited by 2 publications
(6 citation statements)
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“…shows the ROC of different methods, with 400 sub-fingerprints and 3 segmentations. From these figures, we can see that, our extraction method has better ROC performances than other extraction methods, which has a result same to that of [5]. The reason why our method can be better others is that our fingerprint method is more effective than these methods [1][2][7] [9], therefore results in good performances for video copyright detection, with same conditions.…”
Section: Discussionsupporting
confidence: 61%
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“…shows the ROC of different methods, with 400 sub-fingerprints and 3 segmentations. From these figures, we can see that, our extraction method has better ROC performances than other extraction methods, which has a result same to that of [5]. The reason why our method can be better others is that our fingerprint method is more effective than these methods [1][2][7] [9], therefore results in good performances for video copyright detection, with same conditions.…”
Section: Discussionsupporting
confidence: 61%
“…[4], which employ a pairwise boosting on a large set of Viola-Jones features [12], to extract audio fingerprints by learning compact, discriminative, local descriptors on audio spectrograms [4], we has proposed a video fingerprint method, which employ a modified pairwise boosting on visual Viola-Jones features [12], to learn topdiscriminative filters for video fingerprinting [5] [6]. The top four Harr features learned for video fingerprint's extraction are shown in Fig.…”
Section: Motivated By the Work Of Kementioning
confidence: 99%
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