Algorithms

K-Nearest Neighbors: Distance Metric

L1(manhattan) distance

alternate text

< L1, L2, Chebyshev distances >

\[d_1(I_1, I_2) = \sum_{p} \left|I_1^p - I_2^p\right|\]

L1 value may change if the coordinate system changes.

L2(Euclidean) distance

\[d_1(I_1, I_2) = \sqrt{\sum_{p} (I_1^p - I_2^p)^2}\]

More generic. Values stay the same even when the coordinate system changes.

Disadvantage

  • The classifier must remember all of the training data and store it for future comparisons
  • Not suitable for images. Classifying a test image is expensive since it requires a comparison to all training images and images contain millions of pixels. Don’t even talk about videos.