Learning-based automatic commercial content detection
First Claim
Patent Images
1. A method for learning-based automatic commercial content detection, the method comprising:
- dividing program data into multiple segments;
analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content; and
wherein the context-based features are a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments; and
wherein the single-side left and/or right neighborhoods are (2n+1) neighborhoods of a current segment Ci, and wherein the method further comprises;
calculating each of the (2n+1) neighborhoods as follows;
wherein Nk represents 2n+1, n representing a number of neighborhoods left and/or right of Ci, [si, ei] denoting start and end frame numbers for Ci and start and end times for Ci, Nks represents a start frame number for Nk, Nke represents an end frame number for Nk, L indicating a length of the program data, kε
Z,|k|≦
n, and α
a comprising a time step.
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Abstract
Systems and methods for learning-based automatic commercial content detection are described. In one aspect, program data is divided into multiple segments. The segments are analyzed to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content. The context-based features are a function of single-side left and/or right neighborhoods of segments of the multiple segments.
72 Citations
42 Claims
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1. A method for learning-based automatic commercial content detection, the method comprising:
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dividing program data into multiple segments; analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content; and wherein the context-based features are a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments; and wherein the single-side left and/or right neighborhoods are (2n+1) neighborhoods of a current segment Ci, and wherein the method further comprises; calculating each of the (2n+1) neighborhoods as follows; wherein Nk represents 2n+1, n representing a number of neighborhoods left and/or right of Ci, [si, ei] denoting start and end frame numbers for Ci and start and end times for Ci, Nks represents a start frame number for Nk, Nke represents an end frame number for Nk, L indicating a length of the program data, kε
Z,|k|≦
n, and α
a comprising a time step.- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for learning-based automatic commercial content detection, the method comprising:
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dividing program data into multiple segments; analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content, the context-based features being a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments, the analyzing comprising further; a calculating the context-based feature sets from segment-based visual features as an average value of visual features of Sk, Sk representing a set of all segments of the multiple segments that are partially or totally included in the single-side left and/or right neighborhoods such that Sk={Cjk;
0≦
j<
Mk}={Ci;
Ci∩
Nk≠
Φ
}, Mk being a number of segments in Sk, and wherein Nk represents 2n+1 neighborhoods, n represents a number of neighborhoods left and/or right of a current segment Ci, Sk is a set of segments that are partially or totally included in Nk, Ckj represents is a j-th element of Sk, Mk represents a total number of elements in Sk, and Φ
represents an empty set.
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12. A computer-readable medium for learning-based automatic commercial content detection, the computer-readable medium comprising computer-program executable instructions executable by a processor for:
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dividing program data into multiple segments; analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content, the context-based features being a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments; and calculating the context-based feature sets from segment-based visual features as an average value of visual features of Sk, Sk representing a set of all segments of the multiple segments that are partially or totally included in the single-side left and/or right neighborhoods such that Sk={Cjk;
0≦
j<
Mk}={Ci;
Ci∩
Nk≠
Φ
}, Mk being a number of segments in Sk, andwherein Nk represents 2n+1 neighborhoods, n represents a number of neighborhoods left and/or right of a current segment Ci, Sk is a set of segments that are partially or totally included in Nk, Ckj represents is a j-th element of Sk, Mk represents a total number of elements in Sk, and Φ
represents an empty set. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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21. A computer-readable medium for learning-based automatic commercial content detection, the computer-readable medium comprising computer-program executable instructions executable by a processor for:
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dividing program data into multiple segments; analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content the context-based features being a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments, the single-side left and/or right neighborhoods are (2n+1) neighborhoods of a current segment C; and calculating each of the (2n+1) neighborhoods as follows; wherein Nk represents 2n+1, n representing a number of neighborhoods left and/or right of Ci, [si, ei] denoting start and end frame numbers for Ci and start and end times for Ci, Nks represents a start frame number for Nk, Nke represents an end frame number for Nk, L indicating a length of the program data, kε
Z,|k|≦
n, and α
comprising a time step.
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22. A computing device for learning-based automatic commercial content detection, the computing device comprising:
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a processor; and a memory coupled to the processor, the memory comprising computer-program executable instructions executable by the processor for; dividing program data into multiple segments; analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content, the context-based features being a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments; and wherein the single-side left and/or right neighborhoods are (2n+1) neighborhoods of a current segment Ci, and wherein the computer-program instructions farther comprise instructions for; calculating each of the (2n+1) neighborhoods as follows; wherein Nk represents 2n+1, n representing a number of neighborhoods left and/or right of Ci, [si, ei] denoting start and end frame numbers for Ci and start and end times for Ci, Nks represents a start frame number for Nk, Nke represents an end frame number for Nk, L indicating a length of the program data, kε
Z,|k|≦
n, and α
comprising a time step.- View Dependent Claims (23, 24, 25, 26, 27, 28, 29, 30)
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31. A computing device for learning-based automatic commercial content detection, the computing device comprising:
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a processor; and a memory coupled to the processor, the memory comprising computer-program executable instructions executable by the processor for; dividing program data into multiple segments; analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content, the context-based features being a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments, the analyzing comprising; calculating context-based feature sets from segment-based visual features as an average value of visual features of Sk, Sk representing a set of all segments of the multiple segments that are partially or totally included in the single-side left and/or right neighborhoods such that Sk={Cjk;
0≦
j<
Mk}={Ci;
Ci∩
Nk≠
Φ
}, Mk being a number of segments in Sk, andwherein Nk represents 2n+1 neighborhoods, n represents a number of neighborhoods left and/or right of a current segment Ci, Sk is a set of segments that are partially or totally included in Nk, Ckj represents is a j-th element of Sk, Mk represents a total number of elements in Sk, and Φ
represents an empty set.
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32. A computing device for learning-based automatic commercial content detection, the computing device comprising:
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means for dividing program data into multiple segments; means for analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content; and wherein the context-based features are a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments, the single-side left and/or right neighborhoods being (2n+1) neighborhoods of a current segment C; and wherein the computing device further comprises; means for calculating each of the (2n+1) neighborhoods as follows; wherein n represents a number of neighborhoods left and/or right of Ci, [si, ei] denoting start and end frame numbers for Ci and start and end times for Ci, Nks denotes a staff frame number of Nk, Nke denotes an end frame number of Nk, L indicates a length of the program data, kε
Z,|k|≦
n, and α
comprises a time step.- View Dependent Claims (33, 34, 35, 36, 37, 38, 39, 40, 41)
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42. A computing device for learning-based automatic commercial content detection, the computing device comprising:
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means for dividing program data into multiple segments; means for analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content, the context-based features being a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments; means for calculating the context-based feature sets from segment-based visual features as an average value of visual features of Sk, Sk representing a set of all segments of the multiple segments that are partially or totally included in the single-side left and/or right neighborhoods such that Sk={Cjk;
0≦
j<
Mk}={Ci;
Ci∩
Nk≠
Φ
}, Mk being a number of segments in Sk, andwherein Nk represents 2n+1 neighborhoods, n represents a number of neighborhoods left and/or right of a current segment Ci, Sk is a set of segments that are partially or totally included in Nk, Ckj represents is a j-th element of Sk, Mk represents a total number of elements in Sk, and Φ
represents an empty set.
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Specification