Cognitive Services in Vidinet

Create Intelligent Workflows with Cognitive Services

Being able to recognize content and objects and to understand a spoken language has set us apart from computer capabilities since the dawn of computers. Although we can't crunch numbers in the same speed as a computer, we are aware of the story in the film, we can appreciate and describe contents of the media files, we can understand languages, and we recognize actors. All these skills are examples of cognitive abilities - from the word "recognize".

Wouldn't it be great if we could combine these cognitive abilities with the nearly unlimited performance in today's computing environments? Well, it's already happening. With the introduction of machine learning software, it is now possible to train algorithms to recognize media contents the same way as we humans do.

The time has come when we need to start to challenge our perception of what computers can or cannot do. With the development of cognitive machine learning algorithms, CTO's, Content Owners, and technical strategists now have an entirely new set of tools to build the next-generation digital media supply chain.

With the introduction of VidiNet Cognitive Services, VidiCore API and VidiNet become the perfect platform for the job.

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Five examples of using cognitive services to achieve positive ROI - Evolve your business model with machine learning today!

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Why use Cognitive Services from Vidispine?

Vidispine now extends the VidiCore API with a dedicated cognitive services interface, a core services technology framework allowing you as a customer direct access to a growing number of partners offering cognitive services on the market. In VidiCore API, we have defined a standard structure for the cognitive metadata from different providers. Because of this, customers don’t have to care about how to model and integrate the different metadata results coming back from various providers.

How Can Cognitive Services Improve You Existing Media Supply Chain?

Automatic Subtitling

A machine learning algorithm can do the main part of the job, and a human can make the last changes. Depending on the environment a machine learning transcription algorithm can manage to at least 80-90 % accuracy. Imagine the time spent in manual transcribes for subtitling.

Face recognition

Being able to train the algorithm to detect faces can prove very useful for tracking actors in a production environment in a ingest process. This type of tracking is, of course, also use full for determining the amount of time a person has participated in or finding all clips in an archive with a specific person.

Maybe you want to know what a certain person has said about a certain topic? In such a case you would use a combination of automatic transcription and face recognition.

Image recognition

Collecting data about image contents is of course valuable. Find all shots from the outside location with a certain actor, or detect the presence of a brand in a movie? The current state of Vidispine Cognitive Services relies on the AWS image recognition algorithm and will track objects available in your video with a confidence value to be used as a factor of accuracy.

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Contact us to get a free demo of cognitive services in VidiNet, if you have any questions, or let us help you create a customized trial based on your needs. You can find the contact info below.

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Webinar: Basics of VidiNet Cognitive Services

This webinar gives insights about our AI strategy and how the integration in the VidiNet ecosystem will work. We also demonstrate the first integrations in action and take an in-depth look at the defined metadata model, the Analyzed Data Units.

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