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.

通过像素为基础的判断和模型重构，本文探讨了说同异不同判断和图像不确定性如何影响感知分割和各个特征的权重，并将其应用于自然图像和复合材料纹理的分割，以对感知理论进行定量测试和创造分割算法的新基准。