Abstract: To explore the detailed texture information of fine-grained images at different granularity levels and to focus on the relationships between hierarchical features, a hierarchical multi-granularity image classification method that integrates multi-scale information and feature mapping relationships is proposed. Firstly, mid-level semantic features extracted from the backbone network are utilized as local detailed features of images at different category granularities and fused with corresponding high-level semantic features at the same granularities. Then, a feature mapping algorithm is employed to represent the mapping relationships between category hierarchies, enabling the fusion of multi-granularity features across different levels. Finally, a reordering classification loss (RCL) is introduced to enhance classification accuracy across hierarchical categories, while a triplet center loss (TCL) is utilized to minimize the distance between objects and their class centers in the fine-grained feature space and maximize the distance from different class centers. Evaluation results on 3 hierarchical multi-granularity datasets-CUB- 200-2011, FGVC-Aircraft, and Stanford Cars-demonstrate that the proposed method achieves fine-grained image classification performance of 88.8%, 94.2%, and 95.1%, respectively, with weighted average precision (wAP) values of 90.4%, 95.1%, and 95.1%. These results fully validate the effectiveness and advanced nature of the proposed method for hierarchical multi-granularity image classification tasks.

Key words: image classification, fine-grained image, hierarchical multi-granularity image, multi-scale information, feature mapping