I'm fired up!

Alrighty! Finally exterminated all of the bugs in the perceptron code. The following are the characteristics of the multilayer perceptron that I use for fire color detection:

• 2 layer
• 1 hidden node
• 3 inputs, where each input is a color channel (e.g. R,G,B or H,S,V etc.)
• trained for 100 epochs and got a mean squared error of 0.0033

More than 1 hidden node don't seem to help much and only end up slowing the classifier. Currently the classifier is trained on RGB color data.

The following is an example of an image with fire in it and how the perceptron-based fire color classifier would label the image pixels:

At first glance you might say that fire color classification fails miserably because it tends to label lots of non fire pixels as being fire. However, the goal of this first classifier was to get a very high true positive rate and a low false negative rate, irregardless of the false positive rate. This is because this classifier will be used in concert with other feature detection algorithms such as motion and maybe texture. These additional classification algorithms will help to reduce the false positive rate.

Lastly, you can see the new color palettes of the positive (fire) data and negative (non-fire) data: