I would like to select the relevant and irrelevant features of an image based on neural network. How do I do that?
It is computationally exprensive, but you could
simulate the fovea. One sensor in the middle,
responsible for one pixel. In the neighborhood
other sensors, responsible for a bigger number
of pixels. The farther from the midpoint,
the greater the area one sensor is responsible
for.
A sensor measures the brightness of a pixel,
either from a grey value image or the
3 channels of RGB. And puts the measured
value into the input layer of a NN.
Learning: could be done supervised. You
click with the mouse pointer on a pixel and
classify it as interesting or noninteresting.
The computer stores a training pattern,
a pair of sensor data and desired output.
After the NN has learned all training
data, learned to classify input vectors,
it can be used to scan the whole image
and map each point via feed-forward.
Regards,
Joachim
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