Quick Review: Google Cloud Drag and Drop

Starting us off is Google Cloud Drag and Drop. This service is a demo version and as such does not require a google account, or any sign in process. To use some of Google’s other machine tagging and learning services a full sign-in process is required.

The Drag and Drop service is very user-friendly, all you need is access to an image and away you go! Once dropped the image is processed and various annotations for the image are produced. These are sorted into six tabs; Faces, Labels, Web, Properties, Safe Search and JSON. The maximum file size of images used is 4MB and a user’s browser must have JavaScript enabled.

For the purpose of this project we have been focusing on the ‘Labels’ which are basically tags. They are shown with a percentage number that represents the likelihood of the label’s accuracy, e.g. Monochrome 97%. This service gave fairly accurate results but did not go in-depth into the images contents. Results returned were things like, Monochrome, Photograph, Crowd. Pretty standard and expected stuff (For a full spreadsheet record of the results click here!). One big issue, with the demo version at least, is that users can only process one image at a time. Ultimately making it unrealistic to use on large scale collections like the Tribune negatives collection.

So, Google Cloud Drag and Drop has its pros and cons. Easy to use, free, no sign-up, generally accurate results but those results are not in-depth, and the input method is not optimised for larger collections. Good for a trial but for extracting useful information from the Tribune negatives collection not realistic.

-Machine Tagging Team

Hello from the Machine Tagging team!

Hello! Coming to you from the Machine Tagging team with a run-down on what we’ve been up to. As the Machine Tagging team, we have been investigating machine tagging programs and their applicability to the NSW State Library’s Tribune negatives collection. Our aim is to find a program that can extract useful historical and cultural heritage data from this collection for future users to take advantage of. Our team chose a set of images, with a broad scope of subject, to investigate. We ran this data set through four different programs, Google Cloud Drag and Drop, IBM Watson, Clarifai, and Imagga. We recorded the results and have reviewed each service.