Exploiting spatial relation for fine-grained image classification

Fine-Grained Image Classification (FGIC) aims to distinguish the images within a subordinate category. Recently, many FGIC methods have been proposed and huge progress has been made in the aspects of part detection and feature learning for FGIC. However, FGIC still remains a challenging task due to the large intra-class variance and small inter-class variance. To classify fine-grained images accurately, this paper proposes to exploit spatial relation to capture more discriminative details for FGIC. The proposed method contains two core modules: part selection module and representation module. The part selection module utilizes intrinsic spatial relation between object parts to select object part pairs with high discrimination power. The representation module exploits the interaction between object parts to describe the selected part pairs and construct a semantic image representation for FGIC. The proposed method is evaluated on CUB-200-2011 and FGVC-Aircraft datasets. Experimental results show that the classification accuracy of the proposed method can reach 85.5% on CUB-200-2011 and 86.9% on FGVC-Aircraft respectively, which exceed comparison methods obviously. (C) 2019 Elsevier Ltd. All rights reserved.