Ways to improve Image Pixel Classification

Here is the problem we are trying to solve:

• Goal is to classify pixels of a colored image into 3 different classes.
• We have a set of manually classified data for training purposes
• Pixels almost do not correlate to each other (each have individual behaviour) - so most likely classification is on each individual pixel and based on it's individual features.
• 3 classes approximately can be mapped to colors of RED, YELLOW and BLACK color families.
• We need to have the system semi-automatic, i.e. 3 parameters to c开发者_运维百科ontrol the probability of the presence of 3 outcomes (for final well-tuning)

Having this in mind:

• Which classification technique will you choose?
• What pixel features will you use for classification (RGB, Ycc, HSV, etc) ?
• What modification functions will you choose for well-tuning between three outcomes.

My first try was based on

• Naive bayes classifier
• HSV (also tried RGB and Ycc)
• (failed to find a proper functions for well-tuning)

Any suggestion? Thanks

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For each pixel in the image try using the histogram of colors the n x n window around that pixel as its features. For general-purpose color matching under varied lighting conditions, I have had good luck with using two-dimensional histograms of hue and saturation with a relatively small number of bins along each dimension. Depending upon your lighting consistency it might make sense for you to directly use the RGB values.

As for the classifier, the manual-tuning requirement is most easily expressed using class weights: parameters that specify the relative costs of false negatives versus false positives. I have only used this functionality with SVMs, but I'm sure you can find implementations of other classifiers that support a similar concept.