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.
Nagore Barrena
Assistant Professor
My research focuses on computer vision and deep learning, with applications to biomedical and natural images.