Trusted Fine-Grained Image Classification through Hierarchical Evidence Fusion

Fine-Grained Image Classification (FGIC) aims to classify images into specific subordinate classes of a superclass. Due to insufficient training data and confusing data samples, FGIC may produce uncertain classification results that are untrusted for data applications. In fact, FGIC can be viewed as a hierarchical classification process and the multilayer information facilitates to reduce uncertainty and improve the reliability of FGIC. In this paper, we adopt the evidence theory to measure uncertainty and confidence in hierarchical classification process and propose a trusted FGIC method through fusing multilayer classification evidence. Comparing with traditional approaches, the trusted FGIC method not only generates accurate classification results but also reduces the uncertainty of fine-grained classification. Specifically, we construct an evidence extractor at each classification layer and extract multilayer (multi-grained) evidence in hierarchical classification. To fuse the extracted multi-grained evidence from coarse to fine, we formulate evidence fusion with the Dirichlet hyper probability distribution and thereby hierarchically decompose the evidence of coarse-grained classes into fine-grained classes to enhance the performance of FGIC and reduce uncertainty. The ablation experiments validate that the hierarchical evidence fusion can improve the precision and also reduce the uncertainty of fine-grained classification. Comparison with the state-of-the-art FGIC methods shows that the proposed method achieves competitive performance.