Particle Swarm Optimization for Picture Analysis

Roland Piquepaille writes "Particle swarm optimization (PSO) is a computer algorithm based on a mathematical model of the social interactions of swarms which was first described in 1995. Now, researchers in the UK and Jordan have carried this swarm approach to photography to 'intelligently boost contrast and detail in an image without distorting the underlying features.' This looks like a clever concept even if I haven't seen any results. The researchers have developed an iterative process where a swarm of images are created by a computer. These images are 'graded relative to each other, the fittest end up at the front of the swarm until a single individual that is the most effectively enhanced.'"

Wow

[Izabael_DaJinn]

I love an article on digital imaging technology that has no pictures. This is 2008. Send out your press release with a photo...of something...anything.

Just wondering

[Smordnys s'regrepsA]

The researchers have developed an iterative process where a swarm of images are created by a computer. These images are 'graded relative to each other, the fittest end up at the front of the swarm until a single individual that is the most effectively enhanced.'

Um... if the computer knew how to tell a good picture from a bad, couldn't it have just created a good picture in the first place? This all seems rather useless/confusing to me.

Re:The only problem...

[Mr2cents]

I wonder why they call it a "swarm approach". I'm always suspicious toward people using the latest buzzword, especially if what they are doing sounds like "sorting". The interesting part is the criterion they use, not how they sort the images.

Re:Just wondering

[TuringTest]

it has to know *specifically* what makes that picture better. Why not use that knowledge to jump to the best picture (that it can define) from the first picture?
Because the algorithm doesn't have that kind of knowledge. In AI-based search we don't know how to define absolute functions of quality, but we know how to define (several) relative dimensions of improvement. (Disclaimer - I do this for a living).

Intelligent search is based on iteratively improving one of those dimensions, just a little bit, one at a time. This goes on until we find a solution that is as good as we can get in all dimensions at once; but we simply don't know how to combine all dimensions to create a formula that maximizes all them, because their relative improvements interact with each other in complex, chaotic ways.

Re:Wow

[SlashWombat]

A pictures worth a thousand words. Strikes me that what they are implying is that take a CCTV (MJPEG/MPEG) and correlating the differing images (frames/fields really). I dont think that manipulation of one CCTV image over and over will ever produce results like that seen on CSI!

Re:The only problem...

[serviscope_minor]

IAAAIR...

Oh god, not another 'Bayesian methods for everything' guy..

I know the type, but...

I have a GA that can outperform a neural network on a particular task

Really? Sounds unlikely to me, because a NN is a function which maps inputs to outputs (sigmoid, sum, sigmoid, sum,...) and is often, but not always optimized with gradient descent. A GA on the other hand is an optimization algorithm. You could optimize an NN with a GA if you wished.

Either way, a mapping function (eg an NN) is not really comparable to a method op optimizing a mapping funcion (eg a GA).