AI auto-tagging is the process in which artificial intelligence is used to tag media files with metadata. This is a modern approach to metadata tagging, which creates a term that describes a keyword or phrase and assigns these metadata tags to a media asset.

In digital asset management, which we described in-depth in a previous blog, metadata tags are used to make content easier to find with search queries. Both internal teams, or external users in the form of partners or customers, can more easily and quickly find the media that’s most relevant to their search. It also helps companies control who can access their content and ultimately distribute it.

In this blog, we’ll discuss these key aspects of AI discovery and indexing of metadata:

How is AI auto-tagging done?
Exploring the power of AI cognitive engines in auto-tagging
What are the benefits of AI auto-tagging?
How to leverage AI auto-tagging 

How is AI auto-tagging done?

Companies with large media archives need AI auto-tagging to ease the lift in accurately and quickly tagging all their digitized content. Without it, they would have to hire a team of interns and use employees to tag this content manually, which would take:

  • Months of not more than a year to complete
  • A lot of overhead to maintain the team for the duration of the project
  • Potentially lead to inaccuracies with so many people touching the assets

Media companies across industries, from sports teams and federations to film studios and news organizations, have years of content that they have accumulated over the years. Most don’t have a complete inventory of their content and struggle to take advantage of their content because it’s hard to find assets and takes a long time to resurface them.

This is easily solvable with metadata, but historically, creating metadata tags has not come without challenges. These challenges include:

  • Lack of standard and metadata strategy for tagging content as multiple people touch these assets, creating inconsistencies
  • Some content ends up tagged while others don’t for various reasons, including a lack of a metadata plan
  • Missing the necessary expertise to implement tagging
  • It’s extremely laborious to tag content manually
  • If content is not tagged accurately, that media asset can become buried in the archive

But this all changes with AI, which can use a variety of cognitive engines to tag media.

Exploring the power of AI cognitive engines in auto-tagging

To understand just how powerful and valuable AI auto-tagging is for understanding and tagging media archives, we must explore some of the cognitive engines that are available with the technology.

Here is a rundown of the top AI engines:

Audio Fingerprinting 

Sometimes called acoustic fingerprinting, audio fingerprinting engines use a specific signature or fingerprint to identify pre-recorded audio snippets contained within audio and video files.

Face Recognition

Face recognition, which is also interchanged with face identification or face ID, analyzes human faces in images and video, scoring them based on how similar they are with known faces.

Speaker Recognition

Speaker recognition, also called speaker identification, can scour through a piece of audio and determine when speakers change and whose those speakers are.

Logo Detection

Used interchangeably with logo recognition, this engine is used to detect and identify images that represent entities such as retailers, sports teams and groups, media networks, products, companies, and other brands within images and videos.

Object Detection

Referred to as object recognition in some instances, object detection engines help detect that one to many objects are found within videos or still images.

What are the benefits of AI auto-tagging?

As you can imagine, these engines offer immense benefits when it comes to tagging media. No matter the type of content you need to be tagged, from images and videos to audio files documents, AI auto-tagging affords these key benefits:

  • Faster and more cost-effective metadata tagging
  • Enables AI discovery of media assets
  • Can tag at a higher volume than human hands
  •  Is scalable to ensure all future content is tagged appropriately
  • Can tag in real-time

For the latter, this is especially beneficial for live events, such as the US Open of Tennis or the Masters Tournament, making content readily available and searchable in a matter of minutes to advertisers and partners. This allows companies to move faster when producing and sharing content, enabling timely engagement with their customers.

How to leverage AI in auto-tagging

Using AI in auto-tagging media allows companies to free up their teams who would have to manually tag content. This makes managing a massive archive not only more cost-effective but helps companies understand more clearly what they have from a content standpoint.

In doing so, it opens the door to them reusing or repurposing content that may still resonate with their audience, saving on production costs and helping extract even greater ROI from their content.

One of the key capabilities of Veritone Digital Media Hub, a media asset management platform powered by Veritone aiWARE, the first OS for AI, is its ability for accurate AI auto metadata tagging.

Using proprietary and third-party cognitive engines of aiWARE, companies can more accurately tag content and understand what they have in their inventory. Only then can companies truly automate, curate, and activate their content so they can easily manage, distribute, and monetize their media.

The post AI Auto-Tagging – What You Need To Know appeared first on Veritone .

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