# Fusion for Evaluation of Image Classification in Uncertain Environments

Source: [Fusion for Evaluation of Image Classification in Uncertain Environments](<https://www.sota2.com/research/paper/68fa5ff11e209131ce9eccef>)

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

Data updated: 2025-10-28T13:56:24.102Z

Authors: Arnaud Martin

Published: 2008-05-26T11:50:25.000Z

## Abstract

We present in this article a new evaluation method for classification and segmentation of textured images in uncertain environments. In uncertain environments, real classes and boundaries are known with only a partial certainty given by the experts. Most of the time, in many presented papers, only classification or only segmentation are considered and evaluated. Here, we propose to take into account both the classification and segmentation results according to the certainty given by the experts. We present the results of this method on a fusion of classifiers of sonar images for a seabed characterization.

## 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/0805.3935>)

[PDF](<https://arxiv.org/pdf/0805.3935.pdf>)