【摘要】 为挖掘细粒度图像在不同粒度下的细节纹理信息，关注层次特征之间的关系，提出了一种融合多尺度信息和特征映射关系的层次多粒度图像分类方法。首先，提取骨干网络的中级语义特征作为图像在不同类别粒度下的局部细节特征，并与对应类别粒度的高级语义特征进行融合。其次，使用特征映射算法表示类别层次之间的映射关系，对各层次的多粒度特征进行融合。最后，提出重排序分类损失RCL来提升各层次类别的分类准确度，利用类别中心三元组损失TCL在细粒度特征空间中将对象与其类中心的距离尽可能拉近，拉远与不同类中心的距离。在CUB-200-2011,FGVC-Aircraft和Stanford Cars这3个层次多粒度数据集上进行的评测结果表明，所提方法的细粒度图像分类准确度分别达到了88.8%,94.2%和95.1%,加权平均精确率wAP分别达到了90.4%,95.1%和95.1%,充分体现了所提方法在层次多粒度图像分类任务上的有效性和先进性。更多还原

【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.更多还原