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公开(公告)号：US11823453B2
公开(公告)日：2023-11-21
申请号：US17590275
申请日：2022-02-01
发明人： Oron Nir , Maria Zontak , Tucker Cunningham Burns , Apar Singhal , Lei Zhang , Irit Ofer , Avner Levi , Haim Sabo , Ika Bar-Menachem , Eylon Ami , Ella Ben Tov
IPC分类号： G06V20/40 , G06F18/214 , G06F18/24 , G06V20/70
CPC分类号： G06V20/41 , G06F18/214 , G06F18/24765 , G06V20/46 , G06V20/70 , G06V20/47
摘要： The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.
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公开(公告)号：US11954893B2
公开(公告)日：2024-04-09
申请号：US17843270
申请日：2022-06-17
发明人： Oron Nir , Maria Zontak , Tucker Cunningham Burns , Apar Singhal , Lei Zhang , Irit Ofer , Avner Levi , Haim Sabo , Ika Bar-Menachem , Eylon Ami , Ella Ben Tov , Anika Zaman
IPC分类号： G06V10/00 , G06F18/214 , G06F18/24 , G06V10/25 , G06V10/774 , G06V20/40
CPC分类号： G06V10/25 , G06F18/2155 , G06F18/24 , G06V10/774 , G06V20/41 , G06V20/46
摘要： The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.
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公开(公告)号：US11366989B2
公开(公告)日：2022-06-21
申请号：US16831105
申请日：2020-03-26
发明人： Oron Nir , Maria Zontak , Tucker Cunningham Burns , Apar Singhal , Lei Zhang , Irit Ofer , Avner Levi , Haim Sabo , Ika Bar-Menachem , Eylon Ami , Ella Ben Tov , Anika Zaman
摘要： The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.
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公开(公告)号：US11270121B2
公开(公告)日：2022-03-08
申请号：US16831353
申请日：2020-03-26
发明人： Oron Nir , Maria Zontak , Tucker Cunningham Burns , Apar Singhal , Lei Zhang , Irit Ofer , Avner Levi , Haim Sabo , Ika Bar-Menachem , Eylon Ami , Ella Ben Tov
摘要： The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.
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公开(公告)号：US20210056362A1
公开(公告)日：2021-02-25
申请号：US16831105
申请日：2020-03-26
发明人： Oron Nir , Maria Zontak , Tucker Cunningham Burns , Apar Singhal , Lei Zhang , Irit Ofer , Avner Levi , Haim Sabo , Ika Bar-Menachem , Eylon Ami , Ella Ben Tov , Anika Zaman
摘要： The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.
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公开(公告)号：US20210056313A1
公开(公告)日：2021-02-25
申请号：US16831353
申请日：2020-03-26
发明人： Oron Nir , Maria Zontak , Tucker Cunningham Burns , Apar Singhal , Lei Zhang , Irit Ofer , Avner Levi , Haim Sabo , Ika Bar-Menachem , Eylon Ami , Ella Ben Tov
摘要： The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.
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