The paper describes a self supervised parallel self
organizing neural network (PSONN) architecture for true color image
segmentation. The proposed architecture is a parallel extension of the
standard single self organizing neural network architecture (SONN)
and comprises an input (source) layer of image information, three
single self organizing neural network architectures for segmentation
of the different primary color components in a color image scene
and one final output (sink) layer for fusion of the segmented color
component images. Responses to the different shades of color components
are induced in each of the three single network architectures
(meant for component level processing) by applying a multilevel
version of the characteristic activation function, which maps the input
color information into different shades of color components, thereby
yielding a processed component color image segmented on the basis
of the different shades of component colors. The number of target
classes in the segmented image corresponds to the number of levels
in the multilevel activation function. Since the multilevel version of
the activation function exhibits several subnormal responses to the
input color image scene information, the system errors of the three
component network architectures are computed from some subnormal
linear index of fuzziness of the component color image scenes at the
individual level. Several multilevel activation functions are employed
for segmentation of the input color image scene using the proposed
network architecture. Results of the application of the multilevel
activation functions to the PSONN architecture are reported on three
real life true color images. The results are substantiated empirically
with the correlation coefficients between the segmented images and
the original images