Global color image segmentation strategies: Euclidean distance vs. vector angle

Abstract

In the past few years, researchers have been increasingly interested in color image segmentation. We analyze two different global image segmentation algorithms each using its own distance metric: k-means and a mixture of principal components (MPC) neural network. The k-means uses Euclidean distance for color comparisons while the MPC neural network uses vector angles. Two variants of the algorithms are examined. The first uses the RGB pixel itself for clustering while the second uses a 3/spl times/3 neighborhood. Preliminary results on a staged scene image are shown and discussed.