Tiny, Blurry Pictures Find the Limits of Computer Image Recognition (arstechnica.com)

A new PNAS paper takes a look at just how different computer and human visual systems are. Humans can figure out that a mangled word "meant" something recognizable while a computer can't. Likewise with images: humans can piece together what a blurry image might depict based on small clues in the picture, where a computer would be at a loss. The authors of the PNAS paper used a set of blurry, tricky images to pinpoint the differences between computer vision models and the human brain.

They used pictures called "minimal recognizable configurations" (MIRCs) that were either so small or so low-resolution that any further reduction would prevent a person from being able to recognize them. The computer models did a better job after they were trained specifically on the MIRCs, but their accuracy was still low compared to human performance. The reason for this, the authors suggest, is that computers can't pick out the individual components of the image whereas humans can. This kind of interpretation is "beyond the capacities of current neural network models," the authors write.

Comments

Not one example?

[nuckfuts]

This story is rather lacking without a single example of what they're talking about.

Re:Not one example?

[SeaFox]

Yeah, the author should ENHANCE this story a bit.

Re:Not one example?

[Dan East]

The story was full of those low resolution samples. They are just 1x1 images. And they're white.

Re:Not one example?

[ShanghaiBill]

Here is a page with some examples.

Here is a PDF of the paper, which has more examples.

I don't think it means much. Instead of showing that humans see better than computers, it really just shows that this one researcher is bad at programming computer vision systems. If he took his dataset, and made it a Kaggle Competition, I think someone would design a computer vision system that would do much better than his.

Re:Not one example?

[wanax]

I'm a professional neuroscientist that specializes in vision research with a computational bent. They used all the main stream, state of the art, openly available object recognition algorithms currently in use. Computer vision is a huge market, with many applications, from the DoD to self-driving cars to image-based searches. I doubt some 5-figure prize is going to out perform the best algorithms several distinct industries and academia have managed to create while being funded to the tune of over a billion a year for the last 10 years or so.

These are serious researchers. If you think you think you can get any type of computer vision that significantly outperforms humans on this type of task, there is a unicorn startup and multiple ultra-high profile publications awaiting you.

And just FYI based on your further post: they used two types of convolutional neural nets (see Methods: Model Versions and Parameters).

Link

[Cow Jones]

This seems to be the project the article is talking about: http://www.wisdom.weizmann.ac.il/~dannyh/Mircs/mircs.html

Re:Link

[Anonymous Coward]

& paper http://www.pnas.org/content/early/2016/02/09/1513198113.full.pdf

It's the retina, not the brain

[Anonymous Coward]

Jerry Lettvin, in the 1960's did experiments on single optical nerve cells that showed how the retina itself enhances and discovers edges. Human vision is not a "pixel image", it's based on collecting and amplifying *edges* and differentials. Until the computer processing and the cameras, themselves, used for computer vision get this built in at the most basic levels of the CCD and immediate processing, a great deal of the most critical data is thrown out before any more sophisticated ""computer brain" can apply its algorithms.

I did the same thing

[rsilvergun]

when I was 13 and I liked it!