Camouflage is primarily context-dependent yet current metrics for camouflaged scenarios overlook this critical factor. Instead, these metrics are originally designed for evaluating general or salient objects, with an inherent assumption of uncorrelated spatial context. In this paper, we propose a new contextualized evaluation paradigm, Context-measure, built upon a probabilistic pixel-aware correlation framework. By incorporating spatial dependencies and pixel-wise camouflage quantification, our measure better aligns with human perception. Extensive experiments across three challenging camouflaged object segmentation datasets show that Context-measure delivers more reliability than existing context-independent metrics. Our measure can provide a foundational evaluation benchmark for various computer vision applications involving camouflaged patterns, such as agricultural, industrial, and medical scenarios. Code is available at https://github.com/pursuitxi/Context-measure.

翻译：伪装本质上依赖于上下文环境，然而当前用于评估伪装场景的指标却忽视了这一关键因素。这些指标最初是为评估通用或显著对象而设计的，其内在假设是空间上下文不相关。本文提出了一种新的语境化评估范式——Context-measure，该范式建立在概率化像素感知关联框架之上。通过融入空间依赖性和像素级伪装量化，我们的度量指标能更好地与人类感知保持一致。在三个具有挑战性的伪装目标分割数据集上进行的大量实验表明，Context-measure比现有的上下文无关指标具有更高的可靠性。该指标可为涉及伪装模式的各类计算机视觉应用（如农业、工业和医疗场景）提供基础性评估基准。代码发布于 https://github.com/pursuitxi/Context-measure。