Reasoning and Algorithm Selection Augmented Symbolic Segmentation
This work addresses semantic segmentation for computer vision by proposing an incremental method to enhance accuracy through algorithm selection.
The paper tackles the problem of symbolic segmentation by framing it as an algorithm selection problem, where a mechanism chooses the best algorithm per case and combines segments into an optimal result, achieving a 2% improvement in accuracy.
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\%.