Video asset classification
First Claim
1. A content classification system comprising:
- a computing platform including a hardware processor and a system memory;
a video asset classification software code stored in the system memory;
the hardware processor configured to execute the video asset classification software code to;
produce a plurality of image clusters by classifying a plurality of images included in a plurality of video clips with at least one of a plurality of video assets known to be included in the plurality of video clips;
identify key features data corresponding respectively to each image cluster of the plurality of image clusters;
segregate the plurality of image clusters into image super-clusters based on the key features data, each of the image super-clusters including one or more of the plurality of image clusters;
identify each of at least some of the image super-clusters with one of the plurality of video assets known to be included in the plurality of video clips; and
train the video asset classification software code using the identified image super-clusters.
3 Assignments
0 Petitions
Accused Products
Abstract
According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.
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Citations
20 Claims
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1. A content classification system comprising:
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a computing platform including a hardware processor and a system memory; a video asset classification software code stored in the system memory; the hardware processor configured to execute the video asset classification software code to; produce a plurality of image clusters by classifying a plurality of images included in a plurality of video clips with at least one of a plurality of video assets known to be included in the plurality of video clips; identify key features data corresponding respectively to each image cluster of the plurality of image clusters; segregate the plurality of image clusters into image super-clusters based on the key features data, each of the image super-clusters including one or more of the plurality of image clusters; identify each of at least some of the image super-clusters with one of the plurality of video assets known to be included in the plurality of video clips; and train the video asset classification software code using the identified image super-clusters. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for use by a content classification system including a computing platform having a hardware processor and a system memory storing a video asset classification software code for execution by the hardware processor, the method comprising:
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producing, using the hardware processor, a plurality of image clusters by classifying a plurality of images included in a plurality of video clips with at least one of a plurality of video assets known to be included in the plurality of video clips; identifying, using the hardware processor, key features data corresponding respectively to each image cluster of the plurality of image clusters; segregating, using the hardware processor, the plurality of image clusters into image super-clusters based on the key features data, each of the image super-clusters including one or more of the plurality of image clusters; identifying, using the hardware processor, each of at least some of the image super-clusters with one of the plurality of video assets known to be included in the plurality of video clips; and training, using the hardware processor, the video asset classification software code using the identified image super-clusters. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer-readable non-transitory medium having stored thereon instructions, which when executed by a hardware processor, instantiate a method comprising:
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producing a plurality of image clusters by classifying a plurality of images included in a plurality of video clips with at least one of a plurality of video assets known to be included in the plurality of video clips; identifying key features data corresponding respectively to each image cluster of the plurality of image clusters; segregating the plurality of image clusters into image super-clusters based on the key features data, each of the image super-clusters including one or more of the plurality of image clusters; identifying each of at least some of the image super-clusters with one of the plurality of video assets known to be included in the plurality of video clips; and training the video asset classification software code using the identified image super-clusters. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification