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Automatic Image Tagging
bignickel writes "Researchers at Penn State have applied for a patent on software that automatically recognizes objects in photos and tags them accordingly. The 'Automatic Linguistic Indexing of Pictures Real-Time' software (catchy name) trained a database using tens of thousands of images, and new images have 15 tags suggested based on comparisons with objects or concepts in the database. Not sure how you identify a 'concept,' and they're only talking about having one correct tag in the top 15, but still cool."
Since you don't know *which* 50% it'll get right, though, you end up having to look at 100% to determine if the system got it right or not. At that point, it's only saving you a few seconds of typing / picking from a drop-down list. :)
The application is obvious, although, I'll admit, their EXACT method isn't. But at it's core, it is basic supervised learning. Feed your classifier a training a set of images that are already tagged. Extract the features of the image and use those features to predict the tags. When the predicted classifications don't match the actual tags, adjust the model, rinse and repeat. Just pick up a data mining book. Like I said, lots of people are working on image classification, and this is an obvious application, at least to those in data mining/machine learning related fields. That doesn't make it an EASY thing to do successfully. If it were easy, there wouldn't be so much research going on. In that sense this group gets my respect for doing a pretty successful job. My concern is the patent. People already look at images and classify them based on content. That's what tagging IS. When computer software is written to automatically do something that every normal person does anyway, should that be patentable? How is this different than people giving Amazon tons of shit for their patents on their product recommender system?
"To lead the people, you must walk behind them"
How can you take a neural network and train it, then patent that?
That's like patenting training a dog to fetch a stick, it's completely rediculous.
You take software capable of generalizing a neural network algorithm by feeding it pictures and associating each picture with certain tags. It then creates a generalized algorithm model based on what you fed it initially. So that when you give new input it is capable of outputting tags most similar to what you initially trained it.
So yes this software can recognize boxes, shapes, other objects, maybe scenes etc and associate them with tags... but ask them how the algorithm works under the hood =) They have no idea... a neural network is like a black box after it has been trained. You feed it input and it gives you output based on it's initial training. The inner workings are chaotic spaghetti values set on each neuron weighting and can't be deciphered.
How can you patent software that is a black box inside?
"Yes hello patent office? I have this box that manufactures microprocessors. I feed it all the materials and it outputs a shiny new processor. I am not sure of the manufacturing process internally but the output works great. I would like to patent this manufacturing process.
"Okay your patent number is 247286-"BLACK BOX"-9
The whole point of a neural network is it generalizes what you train it and can future predict any input based on that.
It's like having the invention of the first mirror and everytime someone put something different infront of it, that person called up the art gallery because they had a new painting that they wanted in their name (because depending what was in front of it you get a different reflection).