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公开(公告)号:US20190243851A1
公开(公告)日:2019-08-08
申请号:US16387443
申请日:2019-04-17
申请人: Gracenote, Inc.
发明人: Jose Pio Pereira , Sunil Suresh Kulkarni , Shashank Merchant , Prashant Ramanathan , Pradipkumar Dineshbhai Gajjar
IPC分类号: G06F16/48 , G06F16/901 , G06F16/783 , G06F16/22 , G06F16/951 , G06K9/00 , G06F16/28 , G06F16/41 , G06K9/46
CPC分类号: G06F16/48 , G06F16/2255 , G06F16/285 , G06F16/41 , G06F16/783 , G06F16/7847 , G06F16/9014 , G06F16/951 , G06K9/00744 , G06K9/4671 , Y10S707/913
摘要: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.
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公开(公告)号:US20190251113A1
公开(公告)日:2019-08-15
申请号:US16387448
申请日:2019-04-17
申请人: Gracenote, Inc.
发明人: Jose Pio Pereira , Sunil Suresh Kulkarni , Shashank Merchant , Prashant Ramanathan , Pradipkumar Dineshbhai Gajjar
IPC分类号: G06F16/48 , G06F16/901 , G06F16/783 , G06F16/22 , G06F16/951 , G06K9/00 , G06F16/28 , G06F16/41 , G06K9/46
CPC分类号: G06F16/48 , G06F16/2255 , G06F16/285 , G06F16/41 , G06F16/783 , G06F16/7847 , G06F16/9014 , G06F16/951 , G06K9/00744 , G06K9/4671 , Y10S707/913
摘要: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.
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3.
公开(公告)号:US10970328B1
公开(公告)日:2021-04-06
申请号:US16140531
申请日:2018-09-24
申请人: Gracenote, Inc.
发明人: Sunil Suresh Kulkarni , Pradipkumar Dineshbhai Gajjar , Jose Pio Pereira , Prashant Ramanathan , Mihailo M. Stojancic , Shashank Merchant
IPC分类号: G06F16/45 , G06F16/48 , G06F16/906 , G06F16/44
摘要: Techniques are described that exclude use of “stop-fingerprints” from media database formation and search query to an automatic content recognition (ACR) systems based on media content fingerprints updated by stop-fingerprint analysis. A classification process is presented which takes in fingerprints from reference media files as an input and produces a modified set of fingerprints as an output by applying a novel stop-fingerprint classification algorithm. Architecture for the distributed stop-fingerprint generation is presented. Various cases, as stop-fingerprints generation for the entire reference database, stop-fingerprints generation for the individual reference fingerprint files, and temporal fingerprint classification obtained through intermediate steps of the temporal fingerprint classification algorithm are presented. A hash-based signature classification algorithm is also described.
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4.
公开(公告)号:US10956484B1
公开(公告)日:2021-03-23
申请号:US15456861
申请日:2017-03-13
申请人: Gracenote, Inc.
发明人: Sunil Suresh Kulkarni , Pradipkumar Dineshbhai Gajjar , Jose Pio Pereira , Prashant Ramanathan , Mihailo M. Stojancic , Shashank Merchant
IPC分类号: G06F16/45 , G06F16/48 , G06F16/906 , G06F16/44
摘要: Techniques are described that exclude use of “stop-fingerprints” from media database formation and search query to an automatic content recognition (ACR) systems based on media content fingerprints updated by stop-fingerprint analysis. A classification process is presented which takes in fingerprints from reference media files as an input and produces a modified set of fingerprints as an output by applying a novel stop-fingerprint classification algorithm. Architecture for the distributed stop-fingerprint generation is presented. Various cases, as stop-fingerprints generation for the entire reference database, stop-fingerprints generation for the individual reference fingerprint files, and temporal fingerprint classification obtained through intermediate steps of the temporal fingerprint classification algorithm are presented. A hash-based signature classification algorithm is also described.
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公开(公告)号:US20190251114A1
公开(公告)日:2019-08-15
申请号:US16387456
申请日:2019-04-17
申请人: Gracenote, Inc.
发明人: Jose Pio Pereira , Sunil Suresh Kulkarni , Shashank Merchant , Prashant Ramanathan , Pradipkumar Dineshbhai Gajjar
IPC分类号: G06F16/48 , G06F16/901 , G06F16/783 , G06F16/22 , G06F16/951 , G06K9/00 , G06F16/28 , G06F16/41 , G06K9/46
CPC分类号: G06F16/48 , G06F16/2255 , G06F16/285 , G06F16/41 , G06F16/783 , G06F16/7847 , G06F16/9014 , G06F16/951 , G06K9/00744 , G06K9/4671 , Y10S707/913
摘要: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.
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公开(公告)号:US09785708B2
公开(公告)日:2017-10-10
申请号:US14718218
申请日:2015-05-21
申请人: GRACENOTE, INC.
发明人: Sunil Suresh Kulkarni , Jose Pio Pereira , Pradipkumar Dineshbhai Gajjar , Shashank Merchant , Prashant Ramanathan , Mihailo M. Stojancic
IPC分类号: G06F17/30
CPC分类号: G06F17/30799 , G06F17/30023 , G06F17/3033 , G06F17/30424 , G06F17/30864
摘要: An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented. A caching mechanism for multimedia search queries in a centralized or in a decentralized distributed system and a client based local multimedia search system enabling multimedia tracking are described.
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