Two-Stage Feature Selection for Fine-Grained Image Recognition Via Partial Order Analysis and Heterogeneity Evaluation

The core challenge of fine-grained image recognition (FGIR) tasks is distinguishing highly similar subclasses within the same base category. Most CNN-based deep learning methods typically focus on extracting information from local regions while overlook the inherent structure between subclasses and the complex relationships between features. This paper presents a two-stage feature selection method based on partial order analysis (POA) and heterogeneity evaluation (HE) for FGIR tasks, guiding the model to focus on distinctive features while reducing uncertainty caused by interfering information. Specifically, in the POA stage, clustering first groups similar subcategories into a medium-granularity category. Formal concept analysis then models their hierarchical partial order, identifying “shared features” among subcategories and “exclusive features” unique to each. This structured representation highlights key contrastive cues. In the HE stage, a novel heterogeneity index is introduced to measure the fluctuation of low-level features within each fine-grained category. This index guides the model to suppress pseudo-discriminative features with high heterogeneity, mitigating the impact of noisy and unstable information on decision-making. We perform comprehensive experiments on three commonly used benchmark datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft). Experimental results show that the proposed method outperforms classic FGIC methods, validating the effectiveness of our approach.