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

EDC3: Ensemble of Deep-Classifiers using Class-specific Copula functions to Improve Semantic Image Segmentation