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.

翻译：将视觉刺激分割为不同的特征组和视觉对象是视觉功能的核心。经典心理物理方法已揭示人类知觉分割的诸多规则，而机器学习的最新进展也催生了成功的算法。然而，人类分割的计算逻辑仍不明确，部分原因在于我们缺乏良好控制的范式来测量知觉分割图并定量比较模型。本文提出一种新的综合方法：针对给定图像，测量基于像素的多个"相同-不同"判断，并通过模型重建底层分割图。该重建对多种实验操控具有鲁棒性，并能捕捉个体参与者的变异性。我们通过自然图像与复合纹理的人类分割实验验证了该方法的有效性。研究表明，图像不确定性会影响测量到的人类变异性，并影响参与者对不同视觉特征的权重分配。由于任何假设的分割算法均可接入进行重建，本范式既可为知觉理论提供定量检验，也可为分割算法建立新的基准。