Measuring uncertainty in human visual segmentation
This work addresses the lack of quantitative methods for comparing perceptual segmentation models in vision science, offering a new benchmark for both human and machine segmentation.
The authors tackled the problem of measuring human visual segmentation uncertainty by proposing a new paradigm that reconstructs segmentation maps from pixel-based same-different judgments, demonstrating its validity on natural images and textures with results showing how image uncertainty affects human variability and feature weighting.
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.