Symbolic Segmentation Using Algorithm Selection

TL;DR

This paper proposes a novel approach to symbolic segmentation by framing it as an algorithm selection problem, which improves segmentation results by choosing the best algorithm for each case.

Contribution

It introduces an algorithm selection framework for symbolic segmentation, replacing traditional single-method approaches with a case-by-case selection mechanism.

Findings

Algorithm selection improves segmentation accuracy significantly.

Experimental results show increased performance over individual algorithms.

Framework demonstrates effectiveness across different experimental setups.

Abstract

In this paper we present an alternative approach to symbolic segmentation; instead of implementing a new method we approach symbolic segmentation as an algorithm selection problem. That is, let there be $n$ available algorithms for symbolic segmentation, a selection mechanism forms a set of input features and image attributes and selects on a case by case basis the best algorithm. The selection mechanism is demonstrated from within an algorithm framework where the selection is done in a set of various algorithm networks. Two sets of experiments are performed and in both cases we demonstrate that the algorithm selection allows to increase the result of the symbolic segmentation by a considerable amount.