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arXiv · 2512.07076

Context-measure: Contextualizing Metric for Camouflage

Chen-Yang Wang·Ge-Peng Ji·Song Shao·Ming-Ming Cheng·Deng-Ping Fan
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Abstract

Camouflage relies heavily on context, but current metrics used in camouflaged object segmentation ignore contextual cues. We identify two major drawbacks of these metrics: first, the Dimension Flaw - a predicted foreground map usually contains both pixel labels and probability scores, whereas ground truth provides only one-dimensional binary labels; second, the Range Flaw - these metrics struggle to capture full-range pixel dependencies. Thus, we propose Context-measure, a novel context-aware evaluation paradigm built on a probabilistic pixel correlation framework. It augments the ground truth with pixel-level contextual affinity and builds a perception cycle, achieving greater consistency with human perception. Extensive experiments using four meta-measures show that our Context-measure comprehensively outperforms all widely adopted metrics for camouflaged object segmentation. To our knowledge, this is the first metric designed for camouflaged scenarios. Code is available at https://github.com/pursuitxi/Context-measure.

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Chen-Yang Wang, Ge-Peng Ji, Song Shao, Ming-Ming Cheng, Deng-Ping Fan. 2025-12-08. Context-measure: Contextualizing Metric for Camouflage. https://arxiv.org/abs/2512.07076

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