Welcome to the last chapter of our Digital Asset Management series. So far, we’ve taken a deeper look into digital, media, brand, and video asset management along with the best practices for metadata tagging, all of which can help you better manage your assets and uncover new revenue streams.

In this chapter, we’ll take a look at AI-powered auto-tagging and discuss these key aspects of AI discovery and indexing of metadata:

What is AI auto-tagging?

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.

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 if not more than a year to complete
  • A lot of overhead to maintain the team for the duration of the project
  • Potential  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 come with challenges. Manual tagging is a tedious and time-consuming process that isn’t always accurate—a lack of standards and metadata strategy for tagging content can lead to inconsistent tagging and missed content, and a team without the necessary expertise to implement tagging could lead to even more issues. If the 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 to known faces. 

Speaker Recognition

Speaker recognition, also called speaker identification, can scour through a piece of audio and determine when speakers change and who 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

Also referred to as object recognition, object detection engines help detect specified objects 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. AI auto-tagging can work with any type of content that needs to be tagged, including images, videos, audio files, documents, and more, ultimately leading to: 

  • Faster and more cost-effective metadata tagging
  • AI discovery of media assets
  • A higher volume of tagged data
  • Assurance that future content will be tagged appropriately
  • Real-time tagging capabilities

For the latter, this is especially beneficial for live events (particularly live sporting events) 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 customer engagement.

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 also 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.

Learn more about Veritone Digital Media Hub