  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.
