PteFBIC: Exploiting Pterylotic Relationship for Fine-Grained Bird Image Classification via Rachidian Orientation...

Fine-grained bird image classification (FBIC) is crucial for ecological monitoring and biodiversity conservation, yet it remains challenging under camouflaged appearances, body occlusions, and arbitrary postures. To address these issues, we propose PteFBIC, which enhances fine-grained discriminability by modeling interregional relationships among pteryla-related appearance cues, including the regional organization of texture and color patterns as well as their cross-region transitions and complementarities. Specifically, we design a pteryla token construction module to generate pteryla-related tokens from an orientation-enhanced feature representation for subsequent relationship modeling. Furthermore, a pteryla relationship mining (PRM) module fuses global visual tokens with pteryla-related tokens to explicitly capture dependencies such as orientation-consistent texture organization, cross-region texture transitions, and complementary appearance variations. In addition, a key cue extraction (KCE) module is introduced to aggregate multiscale discriminative evidence, thereby improving robustness to pose variations and local occlusions. Experiments on CUB-200-2011 and NABirds demonstrate that PteFBIC consistently outperforms a wide range of state-of-the-art (SOTA) methods. The code of PteFBIC is available at https://github.com/she3333/PteFBIC.