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:

receive a first plurality of video clips depicting a plurality of video assets, each of the video clips including a plurality of images and an annotation metadata;

preliminarily classify the images included in the first plurality of video clips with at least one of the plurality of video assets to produce a plurality of image clusters;

identify a key features data corresponding respectively to each image cluster;

segregate the image clusters into image super-clusters based on the key feature data, each image super-cluster including one or more image clusters; and

uniquely identify each of at least some of the image super-clusters with one of the plurality of video assets.

2. The content classification system of claim 1 , wherein uniquely 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 the preliminary classifications producing the one or more image clusters included in the at least one image super-cluster.

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 can be preliminarily 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, the method comprising:

receiving, using the hardware processor, a first plurality of video clips depicting a plurality of video assets, each of the video clips including a plurality of images and an annotation metadata;

preliminarily classifying, using the hardware processor, the images included in the first plurality of video clips with at least one of the plurality of video assets to produce a plurality of image clusters;

identifying, using the hardware processor, a key features data corresponding respectively to each image cluster;

segregating, using the hardware processor, the image clusters into image super-clusters based on the key feature data, each image super-cluster including one or more image clusters; and

uniquely identifying, using the hardware processor, each of at least some of the image super-clusters with one of the plurality of video assets.

9. The method of claim 8 , wherein uniquely 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 the preliminary classifications producing the one or more image clusters included in the at least one image super-cluster.

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 can be preliminarily 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:

receiving a first plurality of video clips depicting a plurality of video assets, each of the video clips including a plurality of images and an annotation metadata;

preliminarily classifying the images included in the first plurality of video clips with at least one of the plurality of video assets to produce a plurality of image clusters;

identifying a key features data corresponding respectively to each image cluster;

segregating the image clusters into image super-clusters based on the key feature data, each image super-cluster including one or more image clusters; and

uniquely identifying each of at least some of the image super-clusters with one of the plurality of video assets.

16. The computer-readable non-transitory medium of claim 15 , wherein uniquely 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 the preliminary classifications producing the one or more image clusters included in the at least one image super-cluster.

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 can be preliminarily 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.