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Graph-based Discriminative Features Learning for Fine-grained Image retrieval

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journal contribution
posted on 2022-11-07, 16:46 authored by H Sun, W Lang, C Xu, N Liu, Huiyu Zhou

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.

Funding

The Fundamental Research Funds for the Central Universities of China under Grant NZ2019009.

History

Author affiliation

School of Informatics, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

Signal Processing: Image Communication

Volume

110

Publisher

Elsevier

issn

0923-5965

Copyright date

2022

Available date

2023-10-18

Language

en

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