KNN Distance Metric Comparisons

I just finished running a comparison of K-nearest neighbor using euclidean distance and chi-squared (I've been using euclidean this whole time). And what do you know, using chi-squared distance got me consistently better results. Here's the results of the tests:

KNN (k=3)

Night Images:

Euclidean Distance:

1. 92% accuracy, # test images = 41
2. 85% accuracy, # test images = 41
3. 87% accuracy, # test images = 41
4. 85% accuracy, # test images = 41
5. 90% accuracy, # test images = 41

Chi-Squared Distance:

1. 92% accuracy, # test images = 41
2. 90% accuracy, # test images = 41
3. 90% accuracy, # test images = 41
4. 90% accuracy, # test images = 41
5. 95% accuracy, # test images = 41

Day Images:

Euclidean Distance:

1. 75% accuracy, # test images = 41
2. 76% accuracy, # test images = 41
3. 81% accuracy, # test images = 41
4. 76% accuracy, # test images = 41
5. 84% accuracy, # test images = 41

Chi-Squared Distance:

1. 77% accuracy, # test images = 41
2. 84% accuracy, # test images = 41
3. 85% accuracy, # test images = 41
4. 78% accuracy, # test images = 41
5. 85% accuracy, # test images = 41

Here are some visual examples of the differences in the results from Chi-Squared and Euclidean Distances:

Chi-Square 1

Euclidean 1

Chi-Square 2

Euclidean 2

Chi-Square 3

Euclidean 3