Fine-grained Image Classification via Multi-scale Selective Hierarchical Biquadratic Pooling

How to extract distinctive features greatly challenges the fine-grained image classification tasks. In previous models, bilinear pooling has been frequently adopted to address this problem. However, most bilinear pooling models neglect either intra or inter layer feature interaction. This insufficient interaction brings in the loss of discriminative information. In this article, we devise a novel fine-grained image classification approach named Multi-scale Selective Hierarchical biQuadratic Pooling (MSHQP). The proposed biquadratic pooling simultaneously models intra and inter layer feature interactions and enhances part response by integrating multi-layer features. The subsequent coarse-to-fine multi-scale interaction structure captures the complementary information within features. Finally, the active interaction selection module adaptively learns the optimal interaction subset for a specific dataset. Consequently, we obtain a robust image representation with coarse-to-fine semantics. We conduct experiments on five benchmark datasets. The experimental results demonstrate that MSHQP achieves competitive or even match the state-of-the-art methods in terms of both accuracy and computational efficiency, with 89.0%, 94.9%, 93.4%, 90.4%, and 91.5% top-1 classification accuracy on CUB-200-2011, Stanford-Cars, FGVC-Aircraft, Stanford-Dog, and VegFru, respectively.