Measuring uncertainty in human visual segmentation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue PLoS Computational Biology Année : 2023

Measuring uncertainty in human visual segmentation

Résumé

Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same–different judgments and perform model–based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures. We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.
Fichier principal
Vignette du fichier
journal.pcbi.1011483-2.pdf (2.41 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Licence : CC BY - Paternité

Dates et versions

hal-04232972 , version 1 (09-10-2023)

Identifiants

Citer

Jonathan Vacher, Claire Launay, Pascal Mamassian, Ruben Coen-Cagli. Measuring uncertainty in human visual segmentation. PLoS Computational Biology, 2023, 19 (9), pp.e1011483. ⟨10.1371/journal.pcbi.1011483⟩. ⟨hal-04232972⟩
3 Consultations
4 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More