  In the literature, many fusion techniques are registered for the segmentation
of images, but they primarily focus on observed output or belief score or
probability score of the output classes. In the present work, we have utilized
inter source statistical dependency among different classifiers for ensembling
of different deep learning techniques for semantic segmentation of images. For
this purpose, in the present work, a class-wise Copula-based ensembling method
is newly proposed for solving the multi-class segmentation problem.
Experimentally, it is observed that the performance has improved more for
semantic image segmentation using the proposed class-specific Copula function
than the traditionally used single Copula function for the problem. The
performance is also compared with three state-of-the-art ensembling methods.
