将图像哈希扩展到局部变化定位，实现高效场景变更检测。
From Image Hashing to Scene Change Detection
- 分块哈希编码+异或聚合，直接在哈希空间做变化检测与定位。
- 无监督训练下达到顶尖性能，计算开销和存储需求大幅降低。
- 适合需要快速视频分析的场景，如监控、自动驾驶等应用。
图像哈希能提供紧凑表示，便于高效存储与检索，但其固有局限在于仅支持全局比较，无法定位变化发生的位置。这一缺陷使其难以直接用于场景变化检测任务，后者要求精确的空间定位。本文从场景变化检测视角重新审视哈希技术，提出 HashSCD——一种分块哈希框架，可在保持高效全局检测的同时实现局部变化定位。HashSCD 将空间对齐的图像块编码为紧凑哈希码，并通过类异或操作聚合，使变化检测与定位可直接在汉明空间完成，无需对历史图像重复推理。模型采用对比学习在块级与全局层级进行无监督训练。实验表明，HashSCD 在性能上媲美当前最先进的无监督哈希与场景变化检测方法，同时显著降低计算成本与存储开销。
原文摘要 · Abstract (English)
Image hashing provides compact representations for efficient storage and retrieval but is inherently limited to global comparison and cannot reason about where changes occur. This limitation prevents hashing from being directly applicable to scene change detection, where spatial localization is essential. In this work, we revisit hashing from a scene change detection perspective and propose HashSCD, a patch-wise hashing framework that enables both efficient global change detection and localized change identification. HashSCD encodes spatially aligned patches into compact hash codes and aggregates them through an XOR-like operation, allowing change detection and localization to be performed directly in the Hamming space without repeated inference on previous images. The model is trained in an unsupervised manner using contrastive learning at both patch and global levels. Experiments demonstrate that HashSCD achieves competitive performance compared to state-of-the-art unsupervised hashing and scene change detection methods, while significantly reducing computational cost and storage requirements.
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