Text2graph VPR: A Text-to-graph Expert System For Explainable Place Recognition In Changing Environments
2025 · Saeideh Yousefzadeh, Hamidreza Pourreza
Abstract
Visual Place Recognition (VPR) in long-term deployment requires reasoning beyond pixel similarity: systems must make transparent, interpretable decisions that remain robust under lighting, weather and seasonal change. We present Text2Graph VPR, an explainable semantic localization system that converts image sequences into textual scene descriptions, parses those descriptions into structured scene graphs, and reasons over the resulting graphs to identify places. Scene graphs capture objects, attributes and pairwise relations; we aggregate per-frame graphs into a compact place representation and perform retrieval with a dual-similarity mechanism that fuses learned Graph Attention Network (GAT) embeddings and a Shortest-Path (SP) kernel for structural matching. This hybrid design enables both learned semantic matching and topology-aware comparison, and -- critically -- produces human-readable intermediate representations that support diagnostic analysis and improve transparency in the dec
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