Join us as we journey through all aspects of Digital Asset Management. Most posts are based on The DAM Book 3.0 – our CPO, Peter Krogh’s essential book on managing digital photo collections.
Start at the beginning of the series or fall in anywhere. Enjoy and feel free to leave us comments.
Learn how to try machine learning out for yourself to see if it is going to help you out or waste your time.
Computational tagging can be done by means of linked or linkable data–using a “key” to connect one set of data to another.
Machine Learning and other AI services can add some useful information to a visual library, but they can only tag for things they “understand”.
Computers use two main Machine Learning methods to automatically tag image files, and it is important to know the differences between them.
Computers can be useful in making sense of a media collection, but what’s the difference between Computational Tagging, Artificial Intelligence, Machine Learning and Deep Learning?
Unlike the IPTC location fields, which provide incomplete and sometimes subjective information about a location, GPS data can provide an objectively precise position.
One of the most useful bits of information you can attach to an image is the location where a photo or video was taken. This information helps us find images, tells us something about the subject matter, and can be a key to lots of other information.
Most standard metadata is used to describe an individual file, but metadata created to describe objects’ relationsh extremely important to preserve.
There are metadata schemas that are specific to a particular application as well as tools to make your own schema to meet personal or institutional needs.
IPTC is the standard schema used by image editing and cataloging software to describe the content and ownership of the pictures.