  In this paper we present an alternative method to symbolic segmentation: we
approach symbolic segmentation as an algorithm selection problem. That is, let
there be a set A of available algorithms for symbolic segmentation, a set of
input features $F$, a set of image attribute $\mathbb{A}$ and a selection
mechanism $S(F,\mathbb{A},A)$ that selects on a case by case basis the best
algorithm. The semantic segmentation is then an optimization process that
combines best component segments from multiple results into a single optimal
result. The experiments compare three different algorithm selection mechanisms
using three selected semantic segmentation algorithms. The results show that
using the current state of art algorithms and relatively low accuracy of
algorithm selection the accuracy of the semantic segmentation can be improved
by 2\%.
