Quaternion Structural-Spectral Aggregation for Training-Free Fine-Grained Leaf Image Retrieval

Training-free fine-grained leaf image retrieval is attractive for continually expanding cultivar collections because it avoids target-specific annotation and fine-tuning. Existing approaches mainly summarize frozen feature maps using real-valued statistics or local pattern transforms, leaving both intra-patch two-dimensional organization and contour-wise cyclic structure underexplored. We propose Quaternion Structural-Spectral Aggregation (QSSA), a training-free descriptor built on multi-scale boundary-aligned patches and frozen CNN features. For each feature channel, QSSA constructs a quadrant-layout quaternion and a symmetry-discrepancy quaternion to encode absolute spatial layout and paired-region relations. The resulting contour-ordered quaternion sequences are aggregated using a left-sided discrete quaternion Fourier transform (DQFT). Quaternion magnitudes provide invariance to the arbitrary contour starting point, while low-frequency truncation suppresses high-frequency variations associated with local boundary perturbations. Experiments on two publicly available fine-grained leaf image retrieval datasets, SoyCultivar200 and PeanCultivar120, demonstrate that QSSA achieves state-of-the-art or competitive performance without target-specific training.