  The ability to characterize the color content of natural imagery is an
important application of image processing. The pixel by pixel coloring of
images may be viewed naturally as points in color space, and the inherent
structure and distribution of these points affords a quantization, through
clustering, of the color information in the image. In this paper, we present a
novel topologically driven clustering algorithm that permits segmentation of
the color features in a digital image. The algorithm blends Locally Linear
Embedding (LLE) and vector quantization by mapping color information to a lower
dimensional space, identifying distinct color regions, and classifying pixels
together based on both a proximity measure and color content. It is observed
that these techniques permit a significant reduction in color resolution while
maintaining the visually important features of images.
