Video asset classification

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.

2. The content classification system of claim 1 , wherein identifying each of the at least one of the image super-clusters with one of the plurality of video assets is based on a confidence value associated with classifications produced by the classifying of the plurality of images.

3. The content classification system of claim 1 , wherein the plurality of video assets depicted in the video clips comprise previously identified video assets.

4. The content classification system of claim 1 , wherein at least some of the first plurality of video clips comprise multiple shots.

5. The content classification system of claim 1 , wherein more than one of the image clusters is classified with a same one of the plurality of video assets depicted in the video clips.

6. The content classification system of claim 1 , wherein the plurality of video assets comprise dramatic characters.

7. The content classification system of claim 1 , wherein the plurality of video assets comprise at least one of objects and locations.

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:

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.

9. The method of claim 8 , wherein identifying at least one of the image super-clusters with one of the plurality of video assets is based on a confidence value associated with classifications produced by the classifying of the plurality of images.

10. The method of claim 8 , wherein the plurality of video assets depicted in the video clips comprise previously identified video assets.

11. The method of claim 8 , wherein at least some of the first plurality of video clips comprise multiple shots.

12. The method of claim 8 , wherein more than one of the image clusters is classified with a same one of the plurality of video assets depicted in the video clips.

13. The method of claim 8 , wherein the plurality of video assets comprise dramatic characters.

14. The method of claim 8 , wherein the plurality of video assets comprise at least one of objects and locations.

15. A computer-readable non-transitory medium having stored thereon instructions, which when executed by a hardware processor, instantiate a method comprising:

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.

16. The computer-readable non-transitory medium of claim 15 , wherein identifying at least one of the image super-clusters with one of the plurality of video assets is based on a confidence value associated with classifications produced by the classifying of the plurality of images.

17. The computer-readable non-transitory medium of claim 15 , wherein the plurality of video assets depicted in the video clips comprise previously identified video assets.

18. The computer-readable non-transitory medium of claim 15 , wherein at least some of the first plurality of video clips comprise multiple shots.

19. The computer-readable non-transitory medium of claim 15 , wherein more than one of the image clusters is classified with a same one of the plurality of video assets depicted in the video clips.

20. The computer-readable non-transitory medium of claim 15 , wherein the plurality of video assets comprise at least one of dramatic characters, objects, and locations.