Video asset classification

Number: 10057644
Published: 2018-08-21
Filed: 2017-04-26
Assignee: Disney Enterprises, Inc.
Inventors: Farre Guiu; Miquel Angel et al.
CPC: H04N21/44008; H04N21/4516; G06V20/41; H04N21/435; H04N21/23418; H04N21/2353
Verdict: Set aside video asset classification/content indexing
Source: Google Patents · FreePatentsOnline

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.

Background

BACKGROUND(1) Video has for some time been, and continues to be a highly popular medium for the enjoyment of entertainment content in the form of movie, television, and sports content, for example, as well as for information content such as news. Due to its popularity with consumers, ever more video content is being produced and made available for distribution. Consequently, the accuracy and efficiency with which video content can be reviewed, classified, archived, and managed has become increasingly important to producers, owners, and distributors of such content. For example, techniques for automating the classification of video content based on features or images included in the video, may reduce the time spent in video production and management.(2) Unfortunately, conventional approaches to automating video classification typically require initial datasets that may be costly and time consuming to prepare. For example, conventional approaches to classifying video content based on image recognition require that collections of precisely labeled images be prepared as an initial input for comparative purposes.SUMMARY(3) There are provided video asset classification systems and methods, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.

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. 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. 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.