A NOVEL PART FEATURE INTEGRATION AND FUSION METHOD FOR FINE-GRAINED VEHICLE RECOGNITION

In this paper, we propose a novel light-weight feature integration and fusion method to enhance the discriminative ability of deep convolutional features for the task of fine-grained vehicle recognition. The proposed method is built on the deep convolutional layers from which the discriminative part features could be integrated and fused accordingly. More specifically, a basic feature integration module is adopted to integrate the feature maps of deep convolutional layers into groups in each of which the related discriminative parts are assembled together. Then a fusion module follows to model the coarse-to-fine relationship of the part features and further ensure the integrity and effectiveness of the part features. We conduct comparison experiments on public dataset, and the results show that the proposed method achieves comparable performance with state-of-the-art algorithms.