Pixel-Level Quality Indicator for Image Data Annotation
Autores: Mikel De La Fuente Landa Nagore Barrena
Fecha: 07.07.2024
Abstract
The creation of image classification and segmentation Machine Learning products requires the annotation of different classes by experts as well as the management of large volumes of data. This paper introduces a new methodology to optimize the computational execution and expert-user contribution by introducing a pixel quality indicator and, therefore reducing the number of annotated data used for model training, based on the geometric information of each pixel. The developed pixel-level quality indicator shows beneficial results, as a result of improving semantic segmentation and classification tasks' performance, validated through the IRIS 1 platform.
BIB_text
title = {Pixel-Level Quality Indicator for Image Data Annotation},
pages = {7492-7496},
keywds = {
Data Annotation Optimization; Pixel Quality; Remote Sensing Segmentation/Classification
}
abstract = {
The creation of image classification and segmentation Machine Learning products requires the annotation of different classes by experts as well as the management of large volumes of data. This paper introduces a new methodology to optimize the computational execution and expert-user contribution by introducing a pixel quality indicator and, therefore reducing the number of annotated data used for model training, based on the geometric information of each pixel. The developed pixel-level quality indicator shows beneficial results, as a result of improving semantic segmentation and classification tasks' performance, validated through the IRIS 1 platform.
}
isbn = {979-835036032-5},
date = {2024-07-07},
}
