Auto Tagging

It seems like we are talking incidental just another function but this is truly ground breaking.  We use a type of Neural Network to achieve auto tagging.  The first reaction I expect is that anybody who  is anybody will say Auto Tagging is impossible.  But we have devised a method over the last four years of research into picture recognition that will really change the way people think about auto tagging.

We have said that currently after a Picture Recognition Search images are tagged so that they can be easily found in the future. Because this tagging is done by a human we know that it is 100% correct or 100% correct for the person who did the tagging. We use this knowledge from previously tagged images to help Auto Tag other images.  But what is impressive about this system is that it is iterative.  Once those new files have been tagged they then can be used to help tag other files and the process continues until all images are tagged. 

The question is asked how can you get around a 99.9% accuracy levels or a default 97%.  (Please note there are intellectual Property rights applied to this explanation.)  When searching we will only initially take the closest overall match in a folder - but can be a set of folders or even the whole disk drive, default is folders. The closest match is then tagged and only if the match is close enough using picture recognition.  On the next iteration the same thing is done over and over again, therefore the jump is very small indeed allowing for a high amount of certainty.  But the end result is that the software will be confident enough to auto tag.

However even within auto tagging you can lower the certainty and allow bigger jumps between images but when doing this it is advised that the results are manually checked.  With Auto Tagging (NO SAMPLES ARE REQUIRED) it just uses image that have already been tagged to search out and tag images that have not yet been tagged.  This can be done in the background  whilst the user gets on with other activities.  Auto Tagging Demo Video

General Picture Recognition Software