Hello Juan, > On 4 Apr 2021, at 18:46, Juan Buhler <[email protected]> wrote: > >> Just out of curiosity, what was the input to the Neural Network? >> Keywords, descriptions? > > No, it's just the pixels themselves. I'm using a pretrained convolutional > neural network, or CNN. You know how Google Photos is able to separate > photos into categories, with dogs, food, mountains, etc? That is done with > a neural network of the same type. > > These CNNs will output the confidence they have that an image belongs to > one of many classes they were trained for. But in the process they compute > a vector that sort of encodes what "features" exist in the image. Features > are things like lines, dots, patterns, and also combinations of things that > might form "higher level features", like eyes, bicycle wheels, etc etc. > These vectors are of very high dimension, in this case 2048. > > It turns out that points in this 2048-D space will be close to each other > if the images they come from are similar to each other. > > The process I'm using computes and saves this vector for each image. That > alone allows me to do image similarity search, by comparing these vectors. > > In order to make the plot, I use a technique that "folds" those 2048 > dimensions into two, so I can find a position for each image on the plane. >
Okay, interesting, thanks for explaining! I have been doing something remotely similar with face-recognition on multiple live video-streams a few years ago, for supermarket customer-following systems ... > Hopefully I succeeded in making that explanation not too technical? > Not at all, I am a programmer too, mainly making disk and filesystem recovery software, in plain ‘C’ ;-) Regards, JvW ------------------------------------------------------------------ Jan van Wijk; https://www.dfsee.com/gallery -- %(real_name)s Pentax-Discuss Mail List To unsubscribe send an email to [email protected] to UNSUBSCRIBE from the PDML, please visit the link directly above and follow the directions.
