We introduce submodular hypergraphs, a family of hypergraphs that have different
submodular weights associated with different cuts of hyperedges. Submodular hypergraphs …

The minimum st cut problem in graphs is one of the most fundamental problems in
combinatorial optimization, and graph cuts underlie algorithms throughout discrete …

Submodular functions are at the core of many machine learning and data mining tasks. The
underlying submodular functions for many of these tasks are decomposable, ie, they are …

Minimizing a sum of simple submodular functions of limited support is a special case of
general submodular function minimization that has seen numerous applications in machine …

This paper bridges discrete and continuous optimization approaches for decomposable
submodular function minimization, in both the standard and parametric settings. We provide …

This paper investigates connections between discrete and continuous approaches for
decomposable submodular function minimization. We provide improved running time …

Hypergraph generalizations of many graph cut problems and algorithms have recently been
introduced to better model data and systems characterized by multiway relationships …

Abstract Higher Order MRF-MAP formulation has been a popular technique for solving many
problems in computer vision. Inference in a general MRF-MAP problem is NP Hard, but can …

In this research semantic segmentation (SS) by deep neural network is used. Segmentation
of contextual information with the use of patch-patch between the regions and background …

In recent years, hypergraph generalizations of many graph cut problems have been
introduced and analyzed as a way to better explore and understand complex systems and …