In our first attempt to apply Kmeans to our images we used the pixel colors as our features. As a result, when we clustered the images into two clusters (to simulate the binary fire vs not fire classifcation) we got two unequal clusters that were worse than just randomly assigning half the data into one cluster and the other half into another.
Then, after pre-processing the images by segmenting them out with Expectation Maximum we got clustering results that were better than average. Where, out of the 15 images, only 5 were clustered incorrectly in Cluster 0. And in Cluster 1, out of the 23 images, 9 were clustered incorrectly. Whereas in the previous classifications, in the 21 images 10 were clustered incorrectly. On average, it appears like applying the EM segmentation increased the accuracy by around 33%.