Video asset classification

• US 10,057,644 B1
• Filed: 04/26/2017
• Issued: 08/21/2018
• Est. Priority Date: 04/26/2017
• Status: Active Grant

• Current Assignee: Disney Enterprises Incorporated (The Walt Disney Company)
• Original Assignee: Disney Enterprises Incorporated (The Walt Disney Company)
• Inventors: Farre Guiu, Miquel Angel, Petrillo, Matthew, Alfaro Vendrell, Monica, Beltran Sanchidrian, Pablo, Martin, Marc Junyent, Swerdlow, Avner, Ettinger, Katharine S., Accardo, Anthony M.
• Primary Examiner(s): Pierorazio, Michael B
• Application Number:
  US15/498,309
• Time in Patent Office: 482 Days
• Field of Search: 725139
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: H04N 21/23418 involving operations for analysing video streams, e.g. detecting features or characteristics television picture signal circuitry for scene change detection H04N5/147; filtering for image enhancement G06T5/00; methods or arrangements for recognising scenes G06V20/00; arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/2353 specifically adapted to content descriptors, e.g. coding, compressing or processing of metadata
• CPC Class Codes: H04N 21/435 Processing of additional data, e.g. decrypting of additional data, reconstructing software from modules extracted from the transport stream
• CPC Class Codes: H04N 21/44008 involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/4516 involving client characteristics, e.g. Set-Top-Box type, software version or amount of memory available

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.

Claims

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.