- Number
- 20240249438
- Published
- 2024-07-25
- Filed
- 2023-01-19
- Assignee
- Disney Enterprises Inc.
- Inventors
- Jacobs; Mitchel et al.
- CPC
- H04N21/816; G06T7/90; G06N5/01; H04N21/23439
- Verdict
- Set aside content-type classification, business
- Source
- Google Patents · FreePatentsOnline
Abstract
A system includes a hardware processor, and a memory storing a software code and at least one machine learning (ML) model trained to distinguish between a plurality of content types. The hardware processor executes the software code to receive a content file including data identifying a dataset contained by the content file as being a first content type of the plurality of content types; predict, using the at least one ML model and the dataset, based on at least one image parameter, a first probability that a content type of the dataset matches the first content type identified by the data; and determine, based on the first probability, that the content type of the dataset (i) is the first content type identified by the data, (ii) is not the first content type identified by the data, or (iii) is of an indeterminate content type.
Background
BACKGROUND
Motion picture and other video-based content production companies typically accept distribution video masters from multiple sources that may use different workflows and production processes, and may target different consumer distribution video formats. Moreover, the creative processes used by different content sources may differ, so that content received from different sources may each have a different appearance, which makes it challenging for a distribution, mastering or quality-control specialist not involved in the creation of the content to be certain that the content is free of flaws. As a result, mistakes that are made during preparation of the distribution master may go undetected, and may undesirably cause the content to appear defective to consumers, or may result in delays, additional costs, or both, related to correcting the flaw at a later stage closer to the release date of the content to consumers. Consequently, there is a need in the art for an automated image analysis solution capable of distinguishing between different content types in order to detect when a mismatch exists between an expected video format of content received from a source and the actual format of that content.
Claims
1. A system comprising: a hardware processor; a system memory storing a software code and at least one machine learning (ML) model trained to distinguish between a plurality of content types; the hardware processor configured to execute the software code to: receive a content file including data identifying a dataset contained by the content file as being a first content type of the plurality of content types; predict, using the at least one ML model and the dataset, based on at least one image parameter, a first probability that a content type of the dataset matches the first content type identified by the data; and determine, based on the first probability, that the content type of the dataset (i) is the first content type identified by the data, (ii) is not the first content type identified by the data, or (iii) is of an indeterminate content type. ||
9. A method for use by a system including a hardware processor and a system memory storing a software code and at least one machine learning (ML) model trained to distinguish between a plurality of content types, the method comprising: receiving, by the software code executed by the hardware processor, a content file including data identifying a dataset contained by the content file as being a first content type of the plurality of content types; predicting, by the software code executed by the hardware processor using at least one MIL model and the dataset, based on at least one image parameter, a first probability that a content type of the dataset matches the first content type identified by the received data; and determining, by the software code executed by the hardware processor based on the first probability, that the content type of the dataset (i) is the first content type identified by the data, (ii) is not the first content type identified by the data, or (iii) is of an indeterminate content type. ||
17. A method for training a machine learning (ML) model to distinguish between a plurality of content types, the method comprising: obtaining a plurality of image datasets; generating, for each of the plurality of image datasets, a respective statistical representation of each of one or more variables for use in detecting an image parameter, to provide a plurality of statistical representations; correlating each of the plurality of statistical representations with one of the plurality of content types; and training the ML model, using the training data, to predict a first probability that a content type of another dataset matches at least one of the plurality of content types.