RAGL-MR: Reliability-Aware Global-Local Metric Retrieval for Optical Image-Based Texture and Material Surface Recognition

Optical image-based texture and material surface recognition is important for computational imaging, intelligent inspection, and material-reference retrieval, where a system often needs not only a closed-set label but also inspectable evidence and an expandable gallery. Scale-varying micro-textures, repeated surface structures, global layout cues, and visually ambiguous categories make this task difficult under non-controlled imaging conditions. This paper presents Reliability-Aware Global-Local Metric Retrieval (RAGL-MR), which maps images and patches into a shared ArcFace metric space, retrieves multi-scale patch neighbors as local surface evidence, compresses whole-image reference galleries using Multi-Prototype Global Distribution Modeling (MP-GDM), and combines branch scores through validation-selected reliability-aware fusion. The framework is evaluated on public texture/material surface image benchmarks used as proxies for surface image analysis rather than as controlled photonic experiments. On the first five official DTD splits, the final fusion reaches 74.06 ± 0.11 Top-1, 92.61 ± 0.61 Top-5, and 96.11 ± 0.23 Top-10; on MINC-2500, it reaches 85.84 ± 0.25 Top-1, 97.63 ± 0.28 Top-5, and 99.15 ± 0.10 Top-10, with additional checks on FMD and KTH-TIPS2-b. Additional perturbation, calibration, large-gallery, and industrial surface-defect checks further examine robustness, reliability, and practical retrieval behavior. These results indicate that RAGL-MR provides an evidence-aware and gallery-extensible retrieval complement for optical material surface image analysis.