Measuring uncertainty in human visual segmentation

TL;DR

This paper introduces a new method to measure and analyze human visual segmentation by reconstructing segmentation maps from pixel-based judgments, enabling better understanding and comparison of perceptual and algorithmic segmentation.

Contribution

It presents an integrated approach for quantifying human segmentation variability and benchmarking segmentation algorithms using model-based reconstruction from pixel judgments.

Findings

Image uncertainty influences human segmentation variability.

Participants weigh visual features differently based on uncertainty.

The method is validated on natural images and textures.

Abstract

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.