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Comment by sjtgraham | Hacker News Reader

Unsupervised learning using deep neural networks, e.g. Google made a face detector using only unlabelled images that trained itself[1]. Seems like a similar approach could be applied to image segmentation, i.e. this problem.

[1] http://static.googleusercontent.com/media/research.google.co...

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Hmm, I think the difficulty would then be how you train the neural network, I'm not sure that'd be an easy task.

I think you'd have to apply a fair amount of pre-processing anyway before you passed it over to the ANN.

I think the difficulty is in the computing power required to train the net, but there has been some progress in that area lately http://techblog.netflix.com/2014/02/distributed-neural-netwo...

You're both right. There are challenges in training a deep-learning neural network, and that training requires a lot of processing power.

We open sourced a pretty cool standalone machine in Java that addresses those issues about a week ago. Looking for feedback...

http://deeplearning4j.org

DeepLearning4j author here. I'd just like to add that despite training neural networks being hard, they are great for understanding data if trained right.

There are a lot of innovations in image processing wrt neural nets specifically. The right neural network can learn everything from scene detection to simple object recognition.

I would highly reccommend taking a look at the neural nets course on coursera to understand some of the use cases.

You could probably do some neat things with Google glass. Feed the computer the images the human pays attention to all day, and let the computer extrapolate objects by how a persons' vision tracks them.

If by "all day" you mean "a couple of hours" that it takes to run down the batteries by capturing and processing live video.

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