Graph-based discriminative features learning for fine-grained image retrieval

Abstract

Fine-grained image retrieval has gradually become a hot topic in computer vision, which aims to retrieve images with the same subcategories from general visual categories. Though fine-grained image retrieval has made a breakthrough with the development of convolutional neural networks, its performance is still limited by the low discriminative feature embedding. To solve this problem, most prior works focus on mining more discriminative features with various strategies. In this paper, we propose a novel graph-based discriminative features learning network for fine-grained image retrieval (GDF-Net). We first design a global fine-grained feature aggregation module, which reconstructs the discriminative features through capturing context correlation based on a K-Nearest Neighbor graph. To reduce storage overhead and speed up retrieval, we further design a semantic hash encoding module, which generates a semantically compact hash code under the guidance of Cauchy quantization loss and bit balance loss. Validated by extensive experiments and ablation studies, our method consistently outperforms state-of-the-art generic retrieval methods as well as fine-grained retrieval methods on three datasets, e.g., CUB Birds, Stanford Dogs and Stanford Cars.