CAT | Metadata
A few days ago I wrote about metadata’s application to distribution. A recent panel discussion at the Rights and Metadata Madness conference outlined some of the challenges and case studies from Rovi, MLB and Viacom outlining their metadata needs and the practices they’ve developed to deal with them.
The article is worth a read, but I’ll highlight the challenge outlined by Michael Jeffrey, VP of market solutions at Rovi:
A feature-length movie with a sports theme and containing content that includes music from other properties can have assets from 20-50 separate entities.
And each of those entities can have restrictions on what the maker of that movie can show, he said, adding that it’s possible you can’t show any beer cans or can’t use an actor in any promotions.
Now let’s add the formatting, duration, and other issues from my earlier post!
Google today launched a new API to help parse natural language. An API is an Application Programming Interface, that developers can use to send data to, and get a response back. Natural Language Parsing is used to understand language that is available in computer-readable form (text). Google’s API joins an increasingly long list of very smart APIs that will understand language, recognize images and much more.
A lot has changed since I last wrote about Advances in Content Recognition late last year.
The Final Cut Pro X Creative Summit is on again in October this year.
Three days of cutting-edge training on the latest FCPX and Motion.
Hear directly from Apple Product Managers. Learn from top industry experts.
Apparently I slip in as an ‘industry expert’ with these sessions
10:30am Saturday Using Transcripts in FCPX
10:30am Sunday Production Kit in a Bag
Metadata is one of the most useful tools we have, if we have the tools to use it! Aside from the obvious problems when no metadata is gathered during the shoot, or insufficient metadata is gathered, other issues arise because there are not always tools in the production chain that use the metadata that has been gathered!
A recent student film was used as a template by the Entertainment Technology Center at USC with the purpose of realizing the long-hoped for promise of production metadata, with some fairly ambitious goals.
The results are interesting and important, particularly considering that this is what I would categorize as Technical metadata, rather than Content metadata.
Although my focus is very much on metadata for production, and in particular Content Metadata, there’s a whole other area of metadata for distribution, built around the EIDR ID and fleshed out largely by Rovi. But there’s another area where metadata will likely have to apply: distribution deliverables.
We were discussing metadata in Final Cut Pro X after dinner last night, as one does, and Greg challenged me to think about the difference between Roles and Keywords (Ranges).
I’d spent time thinking about how best to translate metadata from Lumberjack into FCP X before we gained organizational folders for Keyword Collections in an Event, and was mildly surprised we didn’t have anything we thought would map well to Roles.
And that was the last time I thought about it until last night. It took a minute or two, but then it hit me, and it was totally obvious why there was no place for Roles in a “logging and pre-editing” tool.
Keyword Ranges (and Collections) are for organizing Clips.
Roles are for organizing a Project (timeline), and I guess for exporting information to Producer’s Best Friend where we make good use of Role information.
Just over 7 years ago I started identifying the types of metadata that would be useful in post production. One that particularly excited me was derived metadata: using a computer algorithm to derive useful information for use in post production. At the time the only example I could suggest was deriving location and type of location from GPS data.
I first wrote about derived metadata back at the end of January 2009. Derived metadata uses computer analysis to derive metadata from the video source. There are now technologies for speech-to-text, meaning extraction, facial detection, facial recognition, emotion detection, image recognition, and more. One company has been accumulating these somewhat diverse technologies: Apple.
One of the Final Cut Pro X features that really resonates with me, is Keyword Ranges, and by extension, Keyword Collections. I realize now that this enchantment is because Keyword Ranges are a very pure embodiment of Content Metadata. I also realize now, that I’d been simulating this approach in other software, for as long as I can remember. In order to understand better, we’ll need to take a little trip to the past.
At the current stage of technology development, we are largely limited to adding Content Metadata manually. If we want people described; if we want the scene described; or the action described, we need to add Keywords or Notes to achieve that. I don’t expect that to be the case in the future. Technology from Clarifai and Google give us clues to the future.