A region-based segmentation method for fine-grained classification of cephalopod beaks

Context Fine-grained classification of cephalopod beaks faces significant challenges because of small inter-class variations and large intra-class variations, limiting the accuracy of existing methods for morphologically similar species. Aims To develop a fine-grained classification method that can automatically identify discriminative anatomical regions and improve species identification accuracy. Methods We constructed a dataset of 14,000 images from 700 beak pairs of seven cephalopod species. Building on ResNet101, we combined region grouping with beta prior constraints to achieve adaptive segmentation of key anatomical regions and attention-weighted feature learning. Key results Our method achieved classification accuracies of 97.00 and 97.36% for upper and lower beaks respectively, improving on the best existing model by 2.79–3.79%. The model automatically focused on discriminative features, including lateral wall posterior margin curvature, hood development patterns, and wing structures. Conclusions The proposed weakly supervised regional segmentation approach significantly improves both classification performance and interpretability, with results being highly consistent with biological knowledge. Implications This method provides an efficient solution for cephalopod species identification, with broad application prospects in ecological research and fisheries management.