# Evaluation for Uncertain Image Classification and Segmentation

Source: [Evaluation for Uncertain Image Classification and Segmentation](<https://www.sota2.com/research/paper/68fa5ff11e209131ce9ecd10>)

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

Data updated: 2025-10-28T13:56:24.110Z

Authors: Arnaud Martin, Hicham Laanaya, Andreas Arnold-Bos

Published: 2008-06-11T07:02:45.000Z

## Abstract

Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.

## 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/0806.1796>)

[PDF](<https://arxiv.org/pdf/0806.1796.pdf>)