  Combining the properties of monovariate internal functions as proposed in
Kolmogorov superimposition theorem, in tandem with the bounds wielded by the
multivariate formulation of Chebyshev inequality, a hybrid model is presented,
that decomposes images into homogeneous probabilistically bounded multivariate
surfaces. Given an image, the model shows a novel way of working on reduced
image representation while processing and capturing the interaction among the
multidimensional information that describes the content of the same. Further,
it tackles the practical issues of preventing leakage by bounding the growth of
surface and reducing the problem sample size. The model if used, also sheds
light on how the Chebyshev parameter relates to the number of pixels and the
dimensionality of the feature space that associates with a pixel. Initial
segmentation results on the Berkeley image segmentation benchmark indicate the
effectiveness of the proposed decomposition algorithm.

Computer Vision and Pattern Recognition