This paper presents an innovative approach to enhancing explainable image retrieval, particularly in situations where a fine-tuning set is unavailable. The widely-used SPatial verification (SP) method, despite its efficacy, relies on a spatial model and the hypothesis-testing strategy for instance recognition, leading to inherent limitations, including the assumption of planar structures and neglect of topological relations among features. To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process. We propose bio-inspired saccade and fovea functions to verify the topological consistency among features, effectively circumventing the issues associated with SP's spatial model. Our experimental results demonstrate that our method significantly outperforms SP, achieving state-of-the-art performance in non-fine-tuning retrieval. Furthermore, our approach can enhance performance when used in conjunction with fine-tuned features. Importantly, our method retains high explainability and is lightweight, offering a practical and adaptable solution for a variety of real-world applications.

该论文提出了一种创新的方法来增强可解释的图像检索，特别是在没有微调集的情况下。我们将空间模型替换为拓扑模型，并引入了生物启发的扫视和中央凹函数来验证特征之间的拓扑一致性，从而解决了现有方法的局限性。实验结果显示，我们的方法在非微调检索中显著优于已有方法，并且与微调特征结合使用时可提升性能。重要的是，我们的方法保持了高可解释性，同时具有轻量化的特点，适用于各种实际应用。

无需微调的拓扑RANSAC实例验证与检索