# Reasoning and Algorithm Selection Augmented Symbolic Segmentation

Source: [Reasoning and Algorithm Selection Augmented Symbolic Segmentation](<https://www.sota2.com/research/paper/68fa62b81e209131ce9fad23>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:25:18.953Z

Authors: Martin Lukac, Kamila Abdiyeva, Michitaka Kameyama

Published: 2016-08-12T03:42:28.000Z

## Abstract

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\\%.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1608.03667>)

[PDF](<https://arxiv.org/pdf/1608.03667.pdf>)