Abstract :
The extraction of high-level color descriptors is an increasingly
important problem, as these descriptions often provide
links to image content. When combined with image segmentation,
color naming can be used to select objects by color, describe the
appearance of the image, and generate semantic annotations. This
paper presents a computational model for color categorization and
naming and extraction of color composition. In this paper, we start
from the National Bureau of Standards’ recommendation for color
names, and through subjective experiments, we develop our color
vocabulary and syntax. To assign a color name from the vocabulary
to an arbitrary input color, we then design a perceptually
based color-naming metric. The proposed algorithm follows relevant
neurophysiological findings and studies on human color categorization.
Finally, we extend the algorithm and develop a scheme
for extracting the color composition of a complex image. According
to our results, the proposed method identifies known color regions
in different color spaces accurately, the color names assigned to
randomly selected colors agree with human judgments, and the description
of the color composition of complex scenes is consistent
with human observations.
