Content-based image retrieval using handcraft feature fusion in semantic pyramid

Taheri, Fatemeh; Rahbar, Kambiz; Beheshtifard, Ziaeddin

doi:10.1007/s13735-023-00292-7

Cited by 3 publications

(1 citation statement)

References 47 publications

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“… Chen et al (2023) proposed a semantic pyramid network for merging handcrafted features. Taheri, Rahbar & Beheshtifard (2023) retrieved remote sensing image data by fusing global and local features. Nevertheless, there are fewer studies that employ the aforementioned information in fine-grained images.…”

Section: Discussionmentioning

confidence: 99%

Multi-FusNet: fusion mapping of features for fine-grained image retrieval networks

Cui,

Li,

Liu

et al. 2024

PeerJ Computer Science

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As the diversity and volume of images continue to grow, the demand for efficient fine-grained image retrieval has surged across numerous fields. However, the current deep learning-based approaches to fine-grained image retrieval often concentrate solely on the top-layer features, neglecting the relevant information carried in the middle layer, even though these information contains more fine-grained identification content. Moreover, these methods typically employ a uniform weighting strategy during hash code mapping, risking the loss of critical region mapping—an irreversible detriment to fine-grained retrieval tasks. To address the above problems, we propose a novel method for fine-grained image retrieval that leverage feature fusion and hash mapping techniques. Our approach harnesses a multi-level feature cascade, emphasizing not just top-layer but also intermediate-layer image features, and integrates a feature fusion module at each level to enhance the extraction of discriminative information. In addition, we introduce an agent self-attention architecture, marking its first application in this context, which steers the model to prioritize on long-range features, further avoiding the loss of critical regions of the mapping. Finally, our proposed model significantly outperforms existing state-of-the-art, improving the retrieval accuracy by an average of 40% for the 12-bit dataset, 22% for the 24-bit dataset, 16% for the 32-bit dataset, and 11% for the 48-bit dataset across five publicly available fine-grained datasets. We also validate the generalization ability and performance stability of our proposed method by another five datasets and statistical significance tests. Our code can be downloaded from https://github.com/BJFU-CS2012/MuiltNet.git.

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Section: Discussionmentioning

confidence: 99%