PROJECTION BASED HASHING THAT BALANCES ROBUSTNESS AND SENSITIVITY OF MEDIA FINGERPRINTS
First Claim
1. A method, comprising:
- identifying a plurality of candidate features that are components of one or more of audio or video content and a plurality of candidate projection matrices, wherein each of the candidate projection matrices comprise an array of coefficients that relate to the candidate features;
selecting a subgroup of the projection matrices, based at least in part on an optimized combination of at least two characteristics of the candidate features or the projection matrices; and
deriving fingerprints that uniquely identify the audio or video content from the selected optimized projection matrices subgroup.
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Abstract
Multiple candidate feature components of media content or projection matrices (or other hash functions, e.g., non-linear projections) are identified. Each of the candidate projection matrices (or other hash functions) includes an array of coefficients that relate to the candidate features. A subgroup of the candidate features or the projection matrices (or other hash functions) are selected based at least partially on an optimized combination of at least two characteristics of the candidate features or projection matrices (or other hash functions). Media fingerprints that uniquely identify the media content are derived from the selected optimized subgroup. Optimal projection matrices (or other hash functions) may be designed. Performance or sensitivity (e.g., search time) characteristics of the fingerprints are thus balanced with robustness characteristics thereof.
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Citations
33 Claims
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1. A method, comprising:
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identifying a plurality of candidate features that are components of one or more of audio or video content and a plurality of candidate projection matrices, wherein each of the candidate projection matrices comprise an array of coefficients that relate to the candidate features; selecting a subgroup of the projection matrices, based at least in part on an optimized combination of at least two characteristics of the candidate features or the projection matrices; and deriving fingerprints that uniquely identify the audio or video content from the selected optimized projection matrices subgroup. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method comprising:
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extracting a plurality of N features from media content that comprises one or more of audio or video content, wherein N comprises a first positive integer; computing a set of K optimal projection matrices from the N media content features, wherein K comprises a second positive integer; and deriving media fingerprints from the media content with the computed K optimal projection matrices. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)
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30. A method, comprising:
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identifying a plurality of candidate features that are components of one or more of audio or video content and a plurality of candidate hash functions, wherein each of the candidate hash functions comprise an array of coefficients that relate to the candidate features; selecting a subgroup of the hash functions, based at least in part on an optimized combination of at least two characteristics of the candidate features or the hash functions; and deriving fingerprints that uniquely identify the audio or video content from the selected optimized hash functions subgroup.
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31. A method comprising:
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extracting a plurality of N features from media content that comprises one or more of audio or video content, wherein N comprises a first positive integer; computing a set of K optimal hash functions from the N media content features, wherein K comprises a second positive integer; and deriving media fingerprints from the media content with the computed K optimal hash functions. - View Dependent Claims (32, 33)
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Specification