Slashdot Mirror
Computers With Opinions On Visual Aesthetics
photoenthusiast writes "Penn State researchers launched a new online photo-rating system, code named Acquine (Aesthetic Quality Inference Engine), for automatically determining the aesthetic value of a photo. Users can upload their own photographs for an instant Acquine rating, a score from zero to 100. The system learns to associate extracted visual characteristics with the way humans rate photos based on a lot of previously-rated photographs. It is designed for color natural photographic pictures. Technical publications reveal how Acquine works."
Isn't aesthetic value a hugely personal thing? I mean I looked at some of the photos on the site and the ratings were arse-backwards as far as I was concerned. Generic, boring and frankly badly composed images were getting ~95%, whereas others that I thought were truely exceptional were being ranked in the 50's.
I'm not saying "my opinion is better", just that it seems sort of pointless to assign a value to a picture like this.
You can advertise in this sig from as little as £99.99 a month!
This seems a little far-fetched considering the vagueness of someone liking a photo that another person doesn't. I can't imagine this on something like flickr. I guess this could be some standalone rating that people could use on stock photography sites, where buying something needs to have commercial appeal. Websites such as alamy.com tend to do these things manually, they probably might find some use for this.
Real men read Slashdot articles at -1, bottom up.
From the Acquine website "A rule of thumb is that if the aesthetic quality of a photo is obvious to most people, it may not be worthwhile to seek Acquine's opinion on it because Acquine may assign funny scores in such cases." So in cases where the correct score is obvious, Acquine's score can't be trusted? That rather neatly avoids validation or refutation of Acquine's results. This is suspicious and seems to cast doubt on the trustworthiness of its score in less obvious cases.
So Goate is a better image than the Iwo Jima flag raising photo?
Maybe all the people sending goatse to it has biased its aesthetic judgement.
http://michaelsmith.id.au
How on earth should an algorithm know how to infer the symbolic value of the flag rising image?! As far as I understand the Pulitzer Prize is not about artistic and aesthetic value, but rather about journalistic impact, isn't it?
Am I missing something?
Yes.
The system learns the quality of photo, not the abstractions we place upon it.
The photograph, in strict terms of quality alone, is rather poor and achieves an appropriate rating. It cannot measure the value of the image.
The field of AI is not comprised of a majority of researchers frantically trying to build an expert system that can pass a Turing Test. Visual data is complicated and building a system that can take that information and make use it in a very simplistic manner is non-trivial. Read some of scientific papers published by the authors of Acquine, and you'll see that their methodology (image processing, regression, Bayes' classification, decision trees, support vector machines, classification and regression trees, to name but a few) is anything but trivial.
Not only did they build something novel, but they built a system that does a good job of approximating human response to good/bad photography.
If you want to contest the true novelty of their work, through an academically-inspired claim that they combined existing technologies in a way that isn't terribly novel, rather than creating their own technology, then that's fine. However, the blanket statement that some researchers are trying to do "real work" and that Acquine isn't real work, is a giant red-flag indicating that you likely haven't got the slightest clue what actually goes on in the field of AI. Typically researchers like to tackle problems where the utility of their solutions isn't immediately obvious, the previous link to the RoboCup competition is a perfect example; who cares if you can build a robot that can play soccer? By your reasoning, that would be an incredible waste of time. Except, it's becoming the standard problem for multi-robotic systems research, and a large number of AI researchers are devoting significant time towards building RoboCup teams.
Why?
Simple, pick a real world task for a group of robots that "matters". Now decompose that task into all the subproblems that you would need to solve in order to have a robot complete the main task. Chances are, you're going to run into problems involving self-localization, team-work/cooperation, vision, data fusion, etc... All of those subproblems are being worked on and solved in different ways by researchers in the RoboCup challenge. And chances are, if you choose the methodologies used by the teams that win games you're likely to have chosen the most effective methodologies available in the field.
The true value of research isn't the end-product of each individual research-project. It's the end-product of many "useless" research-projects combined.
Oh god, that woman is John Romero!